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Recent trend in medical imaging modalities and their applications in disease diagnosis: a review

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Abstract

Medical Imaging (MI) plays a crucial role in healthcare, including disease diagnosis, treatment, and continuous monitoring. The integration of non-invasive techniques such as X-ray, Positron Emission Tomography (PET) scan, Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), and Ultrasound, has significantly enhanced medical treatment. MI enables visualization of internal structures without invasive procedures, aiding in the diagnosis of various diseases. The introduction of Medical Image Processing (MIP) has further improved disease prediction, detection, analysis, and evaluation. MIP data is utilized in Machine Learning (ML) and Deep Learning (DL) models to develop intelligent systems that enhance medical assistance and better recognition, because human interpretation of medical images is error prone and exhaustive. However, accuracy is crucial for the provision of high-quality healthcare. This has motivated various works on MI using MIP therefore, this paper emphasizes how these imaging modalities can be used to analyze, model, and manipulate data in order to achieve maximum treatment outcome. Moreover, a comprehensive literature survey is conducted to provide a detailed analysis of the working principles, benefits, and limitations of diverse imaging modalities. It explores state-of-the-art methodologies rooted in MI approaches and highlights potential future developments, challenges, trends, observations, and significant improvements in the field.

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References

  1. Geis J (2007) Medical imaging informatics: How it improves radiology practice today. J Digit Imaging 20(2):99–104. https://doi.org/10.1007/s10278-007-9010-2

    Article  Google Scholar 

  2. Kumar R, Pal R (2018) India achieves who recommended doctor population ratio: A call for paradigm shift in public health Discourse. J Fam Med Prim Care 7(5):841–844. https://doi.org/10.4103/jfmpc.jfmpc-218-18

    Article  Google Scholar 

  3. Abhisheka B, Biswas SK, Purkayastha B (2023) A comprehensive review on breast cancer detection, classification and segmentation using deep learning. Arch Comput Methods Eng 1–30. https://doi.org/10.1007/s11831-023-09968-z

  4. Ker J, Wang L, Rao J, Lim T (2017) Deep learning applications in medical image analysis. IEEE Access 6:9375–9389. https://doi.org/10.1109/ACCESS.2017.2788044

    Article  Google Scholar 

  5. Duncan JS, Ayache N (2000) Medical image analysis: Progress over two decades and the challenges ahead. IEEE Trans Pattern Anal Mach intell 22(1):85–106. https://doi.org/10.1109/34.824822

    Article  Google Scholar 

  6. Han Z, Jian M, Wang GG (2022) ConvUNeXt: An efficient convolution neural network for medical image segmentation. Knowl-Based Syst 253:109512

    Article  Google Scholar 

  7. Jian M, Wang J, Yu H, Wang G, Meng X, Yang L, Yin Y (2021) Visual saliency detection by integrating spatial position prior of object with background cues. Expert Syst Appl 168:114219

    Article  Google Scholar 

  8. Jian M, Wang J, Yu H, Wang GG (2021) Integrating object proposal with attention networks for video saliency detection. Inf Sci 576:819–830

    Article  MathSciNet  Google Scholar 

  9. Lu X, Jian M, Wang X, Yu H, Dong J, Lam KM (2022) Visual saliency detection via combining center prior and U-Net. Multimed Syst 28(5):1689–1698

    Article  Google Scholar 

  10. Jian M, Chen H, Tao C, Li X, Wang G (2023) Triple-DRNet: A triple-cascade convolution neural network for diabetic retinopathy grading using fundus images. Comput Biol Med 155:106631

    Article  Google Scholar 

  11. Jian M, Zhang L, Jin H, Li X (2023) 3DAGNet: 3D Deep attention and global search network for pulmonary nodule detection. Electronics 12(10):2333

    Article  Google Scholar 

  12. Jian M, Wu R, Chen H, Fu L, Yang C (2023) Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image Segmentation. CMES Comput Model Eng Sci 137(1):705–716

    Google Scholar 

  13. Yin Y, Han Z, Jian M, Wang GG, Chen L, Wang R (2023) AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation. Comput Biol Med 107120. https://doi.org/10.1016/j.compbiomed.2023.107120

  14. Das D, Biswas SK, Bandyopadhyay S (2022) A critical review on diagnosis of diabetic retinopathy using machine lea-rningand deep learning. Multimed Tools Appl 81:25613–25655. https://doi.org/10.1007/s11042-022-12642-4

    Article  Google Scholar 

  15. Das D, Biswas SK, Bandyopadhyay S (2022) Perspective of AI system for COVID-19 detection using chest images: a review. Multimed Tools Appl 81:21471–21501. https://doi.org/10.1007/s11042-022-11913-4

    Article  Google Scholar 

  16. Altaf F, Islam SM, Akhtar N, Janjua NK (2019) Going deep in medical image analysis: concepts, methods, Challenges, and future directions. IEEE Access 7:99540–99572. https://doi.org/10.1109/ACCESS.2019.2929365

    Article  Google Scholar 

  17. Charron O, Lallement A, Jarnet D, Noblet V, Clavier JB, Meyer P (2018) Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 95:43–54. https://doi.org/10.1016/j.compbiomed.2018.02.004

    Article  Google Scholar 

  18. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312. https://doi.org/10.1109/TMI.2016.2535302

    Article  Google Scholar 

  19. Momose A (2005) Recent advances in x-ray phase imaging. Japanese J Appl Phys 44(9R):6355 (http://iopscience.iop.org/1347-4065/44/9R/6355)

    Article  Google Scholar 

  20. Hu M, Lin H, Fan Z, Gao W, Yang L, Liu C, Song Q (2020) Learning to recognize chest-x-ray images faster and more efficiently based on multi-kernel depthwise convolution. IEEE Access 8:37265–37274. https://doi.org/10.1109/ACCESS.2020.2974242

    Article  Google Scholar 

  21. Mould RF (1995) The early history of x-ray diagnosis with emphasis on the contributions of physics. Phys Med Biol 40(11):1741–1787

    Article  MathSciNet  Google Scholar 

  22. Arya C, Tiwari R (2016) Expert system for breast cancer diagnosis: A survey. International Conference on Computer Communication and Informatics (ICCCI), pp 1–9. https://doi.org/10.1109/ICCCI.2016.7479940

  23. Akcay S, Breckon T (2022) Towards automatic threat detection: A survey of advances of deep learning within x-ray security imaging. Pattern Recogn 122:108245. https://doi.org/10.1016/j.patcog.2021.108245

    Article  Google Scholar 

  24. Chandra TB, Verma K, Singh BK, Jain D, Netam SS (2021) Coronavirus disease (covid-19) detection in chest x-ray images using majority voting based classifier ensemble. Expert Syst Appl 165:113909. https://doi.org/10.1016/j.eswa.2020.113909

    Article  Google Scholar 

  25. Ovalle-Magallanes E, Avina-Cervantes JG, Cruz-Aceves I, Ruiz-Pinales J (2022) Hybrid classical–quantum convolutional neural network for stenosis detection in x-ray coronary angiography. Exp Syst Appl 189:116112. https://doi.org/10.1016/j.eswa.2021.116112

    Article  Google Scholar 

  26. Milŏsević D, Vodanović M, Galić I, Subăsić M (2022) Automated estimation of chronological age from panoramic dental x-ray images using deep learning. Exp Syst Appl 189:116038. https://doi.org/10.1016/j.eswa.2021.116038

    Article  Google Scholar 

  27. Kim HY, Cho SJ, Baek SJ, Jung SW, Ko SJ (2021) Learning-based image synthesis for hazardous object detection in x-ray security applications. IEEE Access 9:135256–135265. https://doi.org/10.1109/ACCESS.2021.3116255

    Article  Google Scholar 

  28. Vrbanˇciˇc G, Podgorelec V (2022) Efficient ensemble for image-based identification of pneumonia utilizing deep CNN and SGD with warm restarts. Expert Syst Appl 187:115834. https://doi.org/10.1016/j.eswa.2021.115834

    Article  Google Scholar 

  29. Guan B, Yao J, Wang S, Zhang G, Zhang Y, Wang X, Wang M (2022) Automatic detection and localization of thighbone fractures in x-ray based on improved deep learning method. Comput Vis Image Underst 216:103345. https://doi.org/10.1016/j.cviu.2021.103345

    Article  Google Scholar 

  30. Marathe K, Marasinou C, Li B, Nakhaei N, Li B, Elmore JG, Shapiro L, Hsu W (2022) Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification. Comput Biol Med 146:105504. https://doi.org/10.1016/j.compbiomed.2022.105504

    Article  Google Scholar 

  31. Anand S, Singh H, Dash A (2009) Clinical applications of pet and PET-CT. Med J Armed Forces India 65(4):353–358. https://doi.org/10.1016/S0377-1237(09)80099-3

    Article  Google Scholar 

  32. Khan SH (2016) Cancer and positron emission tomography imaging in India”: Vision 2025. Indian J Nucl Med: IJNM 31(4):251. https://doi.org/10.4103/0972-3919.190804

    Article  Google Scholar 

  33. Spiro SG, Buscombe J, Cook G, Eisen T, Gleeson F, O’Brien M, Peake MD, Rowell NP, Seymour R (2008) Ensuring the right pet scan for the right patient. Lung Cancer 59(1):48–56. https://doi.org/10.1016/j.lungcan.2007.07.026

    Article  Google Scholar 

  34. Alessio AM, Kinahan PE, Cheng PM, Vesselle H, Karp JS (2004) Pet/ct scanner instrumentation, challenges, and solutions. Radiol Clin 42(6):1017–1032. https://doi.org/10.1016/j.rcl.2004.08.001

    Article  Google Scholar 

  35. Hashimoto F, Ote K, Onishi Y (2022) Pet image reconstruction incorporating deep image prior and a forward projection model, IEEE Trans Radiat Plasma Med Sci 1–7. https://doi.org/10.1109/TRPMS.2022.3161569

  36. Chen Z, Wu Y, Zhang N, Sun T, Shen Y, Zheng H, Liang D, Wang M, Hu Z (2022) High temporal resolution total-body dynamic pet imaging based on pixellevel time-activity curve correction. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2022.3176097

    Article  Google Scholar 

  37. Qiao X, Jiang C, Li P, Yuan Y, Zeng Q, Bi L, Song S, Kim J, Feng DD, Huang Q (2022) “Improving breast tumor Segmentationin pet via attentive transformation based normalization. IEEE J Biomed Health Inform 26(7):3261–3271. https://doi.org/10.1109/JBHI.2022.3164570

    Article  Google Scholar 

  38. Luo Y, Zhou L, Zhan B, Fei Y, Zhou J, Wang Y, Shen D (2022) Adaptive rectification based adversarial network with spectrum constraint for high-quality pet image synthesis. Med Image Anal 77:102335. https://doi.org/10.1016/j.media.2021.102335

    Article  Google Scholar 

  39. Pontoriero AD, Nordio G, Easmin R, Giacomel A, Santangelo B, Jahuar S, Bonoldi I, Rogdaki M, Turkheimer F, Howes O et al (2021) Automated data quality control in FDOPA brain pet imaging using deep learning. Comput Methods Programs Biomed 208:106239. https://doi.org/10.1016/j.cmpb.2021.106239

    Article  Google Scholar 

  40. Huang Y, Zhu H, Duan X, Hong X, Sun H, Lv W, Lu L, Feng Q (2021) Gapfillrecon net: a cascade network for Simultaneouslypet gap filling and image reconstruction. Comput Methods Progr Biomed 208:10627. https://doi.org/10.1016/j.cmpb.2021.106271

    Article  Google Scholar 

  41. Lu D, Popuri K, Ding G, Balachandar R (2018) Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Med Image Anal 46:26–34. https://doi.org/10.1016/j.media.2018.02.002

    Article  Google Scholar 

  42. Slomka PJ, Pan T, Germano G (2016) Recent advances and future progress in pet instrumentation. Semin Nucl Med 46(1):5–19. https://doi.org/10.1053/j.semnuclmed.2015.09.006

    Article  Google Scholar 

  43. Song T, Chowdhury S, Yang F, Dutta J (2020) Super-resolution PET imaging using convolutional neural networks. IEEE Trans Comput Imaging 6:518–528. https://doi.org/10.1109/TCI.2020.2964229

    Article  Google Scholar 

  44. Wang G (2018) High temporal-resolution dynamic pet image reconstruction using a new spatiotemporal kernel method. IEEE Trans Med Imaging, 38(3). https://doi.org/10.1109/TMI.2018.2869868

  45. Wijdicks E (2018) The first CT scan of the brain: entering the neurologic information age. Neurocrit Care 28(3):273–275. https://doi.org/10.1007/s12028-017-0495-3

    Article  Google Scholar 

  46. Gu J, Shi HS, Han P, Yu J, Ma GN, Wu S (2016) Image quality and radiation dose for prospectively triggered coronary CT angiography: 128-slice single-source CT versus first-generation 64-slice dual-source CT. Sci Rep 6(1):1–7. https://doi.org/10.1038/srep34795

    Article  Google Scholar 

  47. Nikolaou K, Flohr T, Knez A, Rist C, Wintersperger B, Johnson T, Reiser MF, Becker CR (2004) Advances in cardiac CT imaging: 64-slice scanner. Int J Cardiovasc Imaging 20(6):535–540. https://doi.org/10.1007/s10554-004-7015-1

    Article  Google Scholar 

  48. El-Askary NS, Salem MAM, Roushdy MI (2022) Features processing for random forest optimization in lung nodule localization. Expert Syst Appl 193:116489. https://doi.org/10.1016/j.eswa.2021.116489

    Article  Google Scholar 

  49. Velichko E, Shariaty F, Orooji M, Pavlov V, Pervunina T, Zavjalov S, Khazaei R, Radmard R (2022) Development of computer-aided model to differentiate covid-19 from pulmonary edema in lung CT scan: Edecovid-net. Comput Biol Med 141:105172. https://doi.org/10.1016/j.compbiomed.2021.105172

    Article  Google Scholar 

  50. Wang Z, Song J, Su R, Hou M, Qi M, Zhang J, Wu X (2022) Structure-aware deep learning for chronic middle ear Disease. Exp Syst Appl 194:116519. https://doi.org/10.1016/j.eswa.2022.116519

    Article  Google Scholar 

  51. Basu A, Sheikh KH, Cuevas E, Sarkar R (2020) Covid-19 detection from CT scans using a two-stage framework. Expert Syst Appl 193:116377. https://doi.org/10.1016/j.eswa.2021.116377

    Article  Google Scholar 

  52. da Cruz LB, Junior DAD, Diniz JOB, Silva AC, de Almeida JDS, de Paiva AC, Gattass M (2022) Kidney tumor segmentation from computed tomography images using deeplabv3+ 2.5 d model. Expert Syst Appl 192:116270. https://doi.org/10.1016/j.eswa.2021.116270

    Article  Google Scholar 

  53. Neethi A, Niyas S, Kannath SK, Mathew J, Anzar AM, Rajan J (2022) Stroke classification from computed tomography scans using 3d convolutional neural network. Biomed Signal Process Control 76:103720. https://doi.org/10.1016/j.bspc.2022.103720

    Article  Google Scholar 

  54. Potter Y, Yeritsyan D, Mahar S, Wu J, Nazarian A, Vaziri A, Vaziri A (2023) Automated bone tumor segmentation and classification as benign or malignant using computed tomographic imaging. J Digital Imaging, 1–10. https://doi.org/10.1007/s10278-022-00771-z

  55. Sluimer I, Schilham A, Prokop M, Van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 25(4):385–405. https://doi.org/10.1109/TMI.2005.862753

    Article  Google Scholar 

  56. Lubell DL (2005) Drawbacks and limitations of computed tomography. Tex Heart Inst J 32(2):250

    Google Scholar 

  57. Jin M (1998) Electromagnetics in magnetic resonance imaging. IEEE Antennas Propag Mag 40(6):7–22. https://doi.org/10.1109/74.739187

    Article  Google Scholar 

  58. Cosmus T, Parizh M (2010) Advances in whole-body MRI magnets. IEEE Trans Appl Supercond 21(3):2104–2109. https://doi.org/10.1109/TASC.2010.2084981

    Article  Google Scholar 

  59. Rizwan M, Shabbir A, Javed AR, Shabbir M, Baker T, Obe DAJ (2022) Brain tumor and glioma grade classification using Gaussian convolutional neural network. IEEE Access 10:29731–29740. https://doi.org/10.1109/ACCESS.2022.3153108

    Article  Google Scholar 

  60. Yin W, Li L, Wu FX (2022) Deep learning for brain disorder diagnosis based on fMRIimages. Neurocomputing 469:332–345. https://doi.org/10.1016/j.neucom.2020.05.113

    Article  Google Scholar 

  61. Loued-Khenissi L, Doll O, Preuschoff K (2019) An overview of functional magnetic resonance imaging techniques for organizational research. Organ Res Methods 22(1):17–45. https://doi.org/10.1177/1094428118802631

    Article  Google Scholar 

  62. Cruz-Martinez C, Reyes-Garcia CA, Vanello N (2022) A novel event-related FMRIsupervoxels-based representation and its application to schizophrenia diagnosis. Comput Methods Programs Biomed 213:106509. https://doi.org/10.1016/j.cmpb.2021.106509

    Article  Google Scholar 

  63. Musallam AS, Sherif AS, Hussein MK (2022) A new convolutional neural network architecture for automatic detection of brain tumors in magnetic resonance imaging images. IEEE Access 10:2775–2782. https://doi.org/10.1109/ACCESS.2022.3140289

    Article  Google Scholar 

  64. Claux F, Baudouin M, Bogey C, Rouchaud A (2022) Dense, deep learning-based intracranial aneurysm detection on to f MRI using two-stage regularized u-net. J Neuroradiol 1–7. https://doi.org/10.1016/j.neurad.2022.03.005

  65. Tripathi PC, Bag S (2022) A computer-aided grading of glioma tumor using deep residual networks fusion. Comput Methods Programs Biomed 215:106597. https://doi.org/10.1016/j.cmpb.2021.106597

    Article  Google Scholar 

  66. Kashyap S, Zhang H, Rao K, Sonka M (2018) Learning-based cost functions for 3-D and 4-D multi-surface multi-object segmentation of knee MRI: data from the osteoarthritis initiative. IEEE Trans Med Imaging 37(5):1103–1113. https://doi.org/10.1109/TMI.2017.2781541

    Article  Google Scholar 

  67. Zhang L, Li L, Tang M, Huan Y, Zhang X, Zhe X (2021) A new approach to diagnosing prostate cancer through magnetic resonance imaging. Alexandria Eng J 60(1):897–904. https://doi.org/10.1016/j.aej.2020.10.018

    Article  Google Scholar 

  68. Talo M, Yildirim O, Baloglu U, Aydin G, Acharya U (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Imaging and Graph 78:101673. https://doi.org/10.1016/j.compmedimag.2019.101673

    Article  Google Scholar 

  69. Leighton T (2007) What is ultrasound? Prog Biophys Mol Biol 93(3):3–83. https://doi.org/10.1016/j.pbiomolbio.2006.07.026

    Article  Google Scholar 

  70. Routh HF (1996) Doppler ultrasound. IEEE Eng Med Biol Mag 5(6):31–40. https://doi.org/10.1109/51.544510

    Article  Google Scholar 

  71. Yang X, Yu L, Li S, Wen H, Luo D, Bian C, Qin J, Ni D, Heng PA (2018) Towards automated semantic segmentation in prenatal volumetric ultrasound. IEEE Trans Med Imaging 38(1):180–193. https://doi.org/10.1109/TMI.2018.2858779

    Article  Google Scholar 

  72. Carovac A, Smajlovic F, Junuzovic D (2011) Application of ultrasound in medicine. Acta Inform Med 19(3):168. https://doi.org/10.5455/aim.2011.19.168-171

    Article  Google Scholar 

  73. Czerwinski RN, Jones DL, O’Brien WD (1999) Detection of lines and boundaries in speckle images-application to medicalultrasound. IEEE Trans Med Imaging 18(2):126–136. https://doi.org/10.1109/42.759114

    Article  Google Scholar 

  74. Garg V, Sahoo A, Saxena V (2022) Identification of endometrial tuberculosis in infertility using non-subsampled contourlet based convolution neural network. Expert Syst Appl 202:117282. https://doi.org/10.1016/j.eswa.2022.117282

    Article  Google Scholar 

  75. Song D, Zhang Z, Li W, Yuan L, Zhang W (2022) Judgment of benign and early malignant colorectal tumors from ultrasound images with deep multi-view fusion. Comput Methods Programs Biomed 215:106634. https://doi.org/10.1016/j.cmpb.2022.106634

    Article  Google Scholar 

  76. Qi X, Yi F, Zhang L, Chen Y, Pi Y, Chen Y, Guo J, Wang J, Guo Q, Li J et al (2022) Computer-aided diagnosis of breast cancer in ultrasonography images by deep learning. Neurocomputing 472:152–165

    Article  Google Scholar 

  77. Turkoglu I, Arslan A, Ilkay E (2002) An expert system for diagnosis of the heart valve diseases. Expert Syst Appl 23(3):229–236. https://doi.org/10.1016/S0957-4174(02)00042-8

    Article  Google Scholar 

  78. Yang X, Chen Z, Jia X (2022) Deep learning algorithm-based ultrasound image information in diagnosis and treatment of pernicious placenta previa. Comput Math Methods Med. https://doi.org/10.1155/2022/3452176

    Article  Google Scholar 

  79. Tiwari S, Kane L, Koundal D, Jain A, Alhudhaif A, Polat K, Zaguia A, Alenezi F, Althubiti SA (2022) SPOSDS: A smart polycystic ovary syndrome diagnostic system using machine learning. Exp Syst Appl 117592. https://doi.org/10.1016/j.eswa.2022.117592

  80. Luo Y, Huang Q, Li X (2022) X Segmentation information with attention integration for classification of breast tumor in ultrasound image. Pattern Recogn 124:108427. https://doi.org/10.1016/j.patcog.2021.108427

    Article  Google Scholar 

  81. Murtaza G, Shuib L, Abdul Wahab AW, Mujtaba G, Mujtaba G, Nweke HF, Azmi NA (2020) Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artif Intell Rev 53:1655–1720. https://doi.org/10.1007/s10462-019-09716-5

    Article  Google Scholar 

  82. Domingues I, Pereira G, Martins P, Duarte H, Santos J, Abreu PH (2020) Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET. Artif Intell Rev 53:4093–4160. https://doi.org/10.1007/s10462-019-09788-3

    Article  Google Scholar 

  83. Faragallah OS, El-Hoseny H, El-Shafai W, Abd El-Rahman W, El-Sayed HS, El-Rabaie ESM, Geweid GG (2020) A comprehensive survey analysis for present solutions of medical image fusion and future directions”. IEEE Access 9:11358–11371. https://doi.org/10.1109/ACCESS.2020.3048315

    Article  Google Scholar 

  84. Yi X, Walia E, Babyn P (2019) Generative adversarial network in medical imaging: A review. Med Image Anal 58:101552. https://doi.org/10.1016/j.media.2019.101552

    Article  Google Scholar 

  85. Yaqub M, Jinchao F, Arshid K, Ahmed S, Zhang W, Nawaz MZ, Mahmood T (2022) Deep learning-based image reconstruction for different medical imaging Modalities. Comput Math Methods Med. https://doi.org/10.1155/2022/8750648

    Article  Google Scholar 

  86. Zhou SK, Greenspan H, Davatzikos C, Duncan JS, Van Ginneken B, Madabhushi A, Summers RM (2021A) review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proc IEEE 109(5):820–838. https://doi.org/10.1109/JPROC.2021.3054390

    Article  Google Scholar 

  87. Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9:71194–71209. https://doi.org/10.1109/ACCESS.2021.3079204

    Article  Google Scholar 

  88. Salama WM, Aly MH (2021) Deep learning in mammography images segmentation and classification: Automated CNN approach. Alex Eng J 60(5):4701–4709. https://doi.org/10.1016/j.aej.2021.03.048

    Article  Google Scholar 

  89. Soulami KB, Kaabouch N, Saidi MN (2022) Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network. Biomed Signal Process Control 76:103696. https://doi.org/10.1016/j.bspc.2022.103696

    Article  Google Scholar 

  90. Kawaji K, Nakajo M, Jinguji M, Tani A, Yoshiura T (2022) Application of machine learning analyses using clinical and radiomic features of 18F-FDG PET/CT to predict postoperative recurrence of breast cancer. J Nucl Med 63 2975–12975

  91. Vagenas TP, Economopoulos TL, Sachpekidis C, Dimitrakopoulou-Strauss A, Pan L, Provata A, Matsopoulos GK (2022) A decision support system for the identification of metastases of Metastatic Melanoma using whole-body FDG PET/CT images. IEEE J Biomed Health Inform 27:1397–1408. https://doi.org/10.1109/JBHI.2022.3230060

    Article  Google Scholar 

  92. Dewangan KK, Dewangan DK, Sahu SP, Janghel R (2022) Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. Multimed Tools Appl 81(10):13935–13960. https://doi.org/10.1007/s11042-022-12385-2

    Article  Google Scholar 

  93. Sun L, Tian H, Ge H, Tian J, Lin Y, Liang C, Zhao Y (2023) Cross-attention multi-branch CNN using DCE-MRI to classify breast cancer molecular subtypes. Front Oncol 13. https://doi.org/10.3389/fonc.2023.1107850

  94. Wang X, Wang S, Yin X, Zheng Y (2022) MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med 141:105058. https://doi.org/10.1016/j.compbiomed.2021.105058

    Article  Google Scholar 

  95. Moon WK, Lee YW, Ke HH, Lee SH, Huang CS, Chang RF (2020) Computer-aided diagnosis of breast ultrasound images using ensemble learning from Convolutional neural networks. Comput Methods Programs Biomed 190:105361. https://doi.org/10.1016/j.cmpb.2020.105361

    Article  Google Scholar 

  96. Luo, Y, Huang Q, Li X (2022) Segmentation information with attention integration for classification of breast tumor in ultrasound image. Pattern Recogn 108427. https://doi.org/10.1016/j.patcog.2021.108427

  97. Yan Y, Liu Y, Wu Y, Zhang H, Zhang Y, Meng L (2022) Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images. Biomed Signal Process Control 72:103299. https://doi.org/10.1016/j.bspc.2021.103299

    Article  Google Scholar 

  98. Balaha HM, Saif M, Tamer A, Abdelhay EH (2022) Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer. Neural Comput Appl 34(11):8671–8695. https://doi.org/10.1007/s00521-021-06851-5

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology Silchar, Silchar, India

    Barsha Abhisheka, Saroj Kumar Biswas, Biswajit Purkayastha & Dolly Das

  2. Sorbonne Université, Paris, France

    Alexandre Escargueil

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  1. Barsha Abhisheka
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  2. Saroj Kumar Biswas
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  3. Biswajit Purkayastha
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  4. Dolly Das
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  5. Alexandre Escargueil
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Correspondence to Barsha Abhisheka.

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Abhisheka, B., Biswas, S.K., Purkayastha, B. et al. Recent trend in medical imaging modalities and their applications in disease diagnosis: a review. Multimed Tools Appl 83, 43035–43070 (2024). https://doi.org/10.1007/s11042-023-17326-1

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  • DOI: https://doi.org/10.1007/s11042-023-17326-1

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