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Optimize brain tumor multiclass classification with manta ray foraging and improved residual block techniques

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Abstract

Brain tumor infection can lead to cognitive impairments and neurological deficits. Its effects on the nervous system can result in sensory disturbances and motor dysfunction. Brain tumor infections can trigger inflammation and swelling, leading to severe headaches and affecting memory and concentration. Due to its harmful impact, brain tumors have emerged as a global health concern, necessitating the development of AI-driven tools to aid medical professionals in the early diagnosis and classification of multiple types of brain tumors. Previous research has predominantly relied on convolutional neural network CNN-based techniques to predict brain tumors. However, these approaches often utilized binary datasets and struggled to extract optimal features for effective model generalization. In our proposed method, we introduce a novel utilization of the manta-ray foraging optimizer-MRFO, specifically in aggregation with a fitness approach, while also incorporating improved residual block techniques within a dense network framework. To address this gap in the literature, we present a novel MRFO-based residual method to optimize the hierarchical feature representation of tumors using DenseNet-169.Initially, we adopt a conventional approach by selecting a base Dense model from a set of pre-trained DenseNet models. Subsequently, we utilize the MRFO strategy to explore optimal hyperparameter settings for the DenseNet-169 model. In the subsequent phase, we extend this approach by integrating an enhanced residual block into the optimized DenseNet-169 architecture, aimed at further boosting performance. Through this methodology, we achieve a notable accuracy of 96.40%, coupled with a robust hierarchical feature representation. This demonstrates the efficacy of our proposed model, surpassing state-of-the-art methods on the same dataset. Furthermore, to validate the robustness of our approach, we conduct experiments on four diverse datasets such as MSID (Multi Skin Infection dataset), gastrointestinal multi-infection, Kaggle MRI multiclass, and BR35H MRI binary class dataset. The proposed model consistently maintains high performance with accuracies of 93.10, 97.46, 98.77, and 99.10% respectively.

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References

  1. Osborn, A., Louis, D., Poussaint, T., Linscott, L., Salzman, K.: The 2021 World Health Organization classification of tumors of the central nervous system: what neuroradiologists need to know. Am. J. Neuroradiol. 43(7), 928–937 (2022)

    Article  Google Scholar 

  2. Mahajan, S., Suri, V., Sahu, S., Sharma, M.C., Sarkar, C.: World Health Organization classification of tumors of the central nervous system 5th edition(WHO CNS5): what’s new? Indian J. Pathol. Microbiol. 65(Suppl 1), S5–S13 (2022)

    Google Scholar 

  3. Shanthi, S., Saradha, S., Smitha, J., Prasath, N., Anandakumar, H.: An efficient automatic brain tumor classification using optimized hybrid deep neural network. Int. J. Intell. Netw. 3, 188–196 (2022)

    Google Scholar 

  4. Khan, S.U.R., Asif, S., Bilal, O., Ali, S.: Deep hybrid model for Mpox disease diagnosis from skin lesion images. Int. J. Imaging Syst. Technol. 34(2), e23044 (2024)

    Article  Google Scholar 

  5. Khan, S.U.R., Zhao, M., Asif, S., Chen, X.: Hybrid-NET: a fusion of DenseNet169 and advanced machine learning classifiers for enhanced brain tumor diagnosis. Int. J. Imaging Syst. Technol. 34(1), e22975 (2024)

    Article  Google Scholar 

  6. Khan, S.U.R., Zhao, M., Asif, S., Chen, X., Zhu, Y.: GLNET: global–local CNN's-based informed model for detection of breast cancer categories from histopathological slides. J. Supercomput. 80(6), 7316-7348 (2023)

  7. Kumar, S., Kumar, D.: Human brain tumor classification and segmentation using CNN. Multimed. Tools Appl. 82(5), 7599–7620 (2023)

    Article  Google Scholar 

  8. Fasihi Shirehjini, O., Babapour Mofrad, F., Shahmohammadi, M., Karami, F.: Grading of gliomas using transfer learning on MRI images. Magn. Reson. Mater. Phys. Biol. Med. 36(1), 43–53 (2023)

    Article  Google Scholar 

  9. Joo, B., et al.: Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: glioblastoma, lymphoma, and metastasis. J. Neuroradiol. 50(4), 388–395 (2023)

    Article  Google Scholar 

  10. Bitto, A.K., Bijoy, M.H.I., Yesmin, S., Mia, M.J.: Tumor-TL: a transfer learning approach for classifying brain tumors from MRI images. In: International Conference on Machine Intelligence and Emerging Technologies, pp. 177-186. Springer (2022)

  11. Muhammad, K., Khan, S., Del Ser, J., De Albuquerque, V.H.C.: Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 507–522 (2020)

    Article  Google Scholar 

  12. Al-Antari, M.A., Han, S.-M., Kim, T.-S.: Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput. Methods Progr. Biomed. 196, 105584 (2020)

    Article  Google Scholar 

  13. Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M.: A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst. Signal Process. 39, 757–775 (2020)

    Article  Google Scholar 

  14. Macin, G., et al.: An accurate multiple sclerosis detection model based on exemplar multiple parameters local phase quantization: ExMPLPQ. Appl. Sci. 12(10), 4920 (2022)

    Article  Google Scholar 

  15. Hamid, M.A., Khan, N.A.: Investigation and classification of MRI brain tumors using feature extraction technique. J. Med. Biol. Eng. 40, 307–317 (2020)

    Article  Google Scholar 

  16. Kaplan, K., Kaya, Y., Kuncan, M., Ertunç, H.M.: Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med. Hypotheses 139, 109696 (2020)

    Article  Google Scholar 

  17. Gokulalakshmi, A., Karthik, S., Karthikeyan, N., Kavitha, M.S.: ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifier. Soft. Comput. 24, 18599–18609 (2020)

    Article  Google Scholar 

  18. Kumar, S., Mankame, D.P.: Optimization driven deep convolution neural network for brain tumor classification. Biocybern. Biomed. Eng. 40(3), 1190–1204 (2020)

    Article  Google Scholar 

  19. Saleh, A., Sukaik, R., Abu-Naser, S.S.: Brain tumor classification using deep learning. In: 2020 International Conference on Assistive and Rehabilitation Technologies (iCareTech), pp. 131–136. IEEE (2020)

  20. Thayumanavan, M., Ramasamy, A.: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier. Concurr. Eng. 29(3), 266–274 (2021)

    Article  Google Scholar 

  21. Jiang, S., Gu, Y., Kumar, E.: Magnetic resonance imaging (MRI) brain tumor image classification based on five machine learning algorithms. Cloud Comput. Data Sci. 4 2122–133 (2023)

  22. Satyanarayana, G., Naidu, P.A., Desanamukula, V.S., Rao, B.C.: A mass correlation based deep learning approach using deep convolutional neural network to classify the brain tumor. Biomed. Signal Process. Control 81, 104395 (2023)

    Article  Google Scholar 

  23. Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., Shoaib, M.: A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8, 55135–55144 (2020)

    Article  Google Scholar 

  24. Sharif, M.I., Khan, M.A., Alhussein, M., Aurangzeb, K., Raza, M.: A decision support system for multimodal brain tumor classification using deep learning. Complex Intell. Syst. 8 3007–3020 (2021)

  25. Musallam, A.S., Sherif, A.S., Hussein, M.K.: A new convolutional neural network architecture for automatic detection of brain tumors in magnetic resonance imaging images. IEEE Access 10, 2775–2782 (2022)

    Article  Google Scholar 

  26. Gupta, R.K., Bharti, S., Kunhare, N., Sahu, Y., Pathik, N.: Brain tumor detection and classification using cycle generative adversarial networks. Interdiscipl. Sci. Comput. Life Sci. 14(2), 485–502 (2022)

    Article  Google Scholar 

  27. Khushi, H.M.T., Masood, T., Jaffar, A., Rashid, M., Akram, S.: Improved multiclass brain tumor detection via customized pretrained EfficientNetB7 model. IEEE Access (2023)

  28. Vaiyapuri, T., Jaiganesh, M., Ahmad, S., Abdeljaber, H.A., Yang, E., Jeong, S.-Y.: Ensemble learning driven computer-aided diagnosis model for brain tumor classification on magnetic resonance imaging. IEEE Access (2023)

  29. Neetha, K., Narayan, D.L.: Feature selection using adaptive manta ray foraging optimization for brain tumor classification. Pattern Anal. Appl. 27(2), 29 (2024)

    Article  Google Scholar 

  30. Liu, J., Chen, Y., Liu, X., Zuo, F., Zhou, H.: An efficient manta ray foraging optimization algorithm with individual information interaction and fractional derivative mutation for solving complex function extremum and engineering design problems. Appl. Soft Comput. 150, 111042 (2024)

    Article  Google Scholar 

  31. Gharehchopogh, F.S., Ghafouri, S., Namazi, M., Arasteh, B.: Advances in Manta Ray foraging optimization: a comprehensive survey. J. Bion. Eng. 21 953–990 (2024)

  32. Khan, S.U.R., Asif, S.: Oral cancer detection using feature-level fusion and novel self-attention mechanisms. Biomed. Signal Process. Control 95, 106437 (2024)

    Article  Google Scholar 

  33. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

  34. Zhao, W., Zhang, Z., Wang, L.: Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng. Appl. Artif. Intell. 87, 103300 (2020)

    Article  Google Scholar 

  35. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  36. Tangudu, V.S.K., Kakarla, J., Venkateswarlu, I.B.: COVID-19 detection from chest X-ray using MobileNet and residual separable convolution block. Soft. Comput. 26(5), 2197–2208 (2022)

    Article  Google Scholar 

  37. Shaik, N.S., Cherukuri, T.K.: Multi-level attention network: application to brain tumor classification. SIViP 16(3), 817–824 (2022)

    Article  Google Scholar 

  38. Essa, E., Aldesouky, D., Hussein, S.E., Rashad, M.Z.: Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation. Med. Biol. Eng. Comput. 58, 2161–2175 (2020)

    Article  Google Scholar 

  39. Paul, J.S., Plassard, A.J., Landman, B.A., Fabbri, D.: Deep learning for brain tumor classification. In: Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 10137, pp. 253–268. SPIE (2017)

  40. Khawaldeh, S., Pervaiz, U., Rafiq, A., Alkhawaldeh, R.S.: Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Appl. Sci. 8(1), 27 (2017)

    Article  Google Scholar 

  41. Sharif, M., Tanvir, U., Munir, E.U., Khan, M.A., Yasmin, M.: Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J. Ambient Intell. Humaniz. Comput. 15 1063–1082 (2018)

  42. Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Alassafi, M.O.: Brain tumor classification based on fine-tuned models and the ensemble method. Comput. Mater. Continua 67(3) (2021)

  43. Ayadi, W., Charfi, I., Elhamzi, W., Atri, M.: Brain tumor classification based on hybrid approach. Vis. Comput. 38(1), 107–117 (2022)

    Article  Google Scholar 

  44. Deepak, S., Ameer, P.: Automated categorization of brain tumor from MRI using CNN features and svm. J. Ambient. Intell. Humaniz. Comput. 12(8), 8357–8369 (2021)

    Article  Google Scholar 

  45. Bodapati, J.D., Shaik, N.S., Naralasetti, V., Mundukur, N.B.: Joint training of two-channel deep neural network for brain tumor classification. SIViP 15(4), 753–760 (2021)

    Article  Google Scholar 

  46. Montalbo, F.J.P.: Diagnosing gastrointestinal diseases from endoscopy images through a multi-fused CNN with auxiliary layers, alpha dropouts, and a fusion residual block. Biomed. Signal Process. Control 76, 103683 (2022)

    Article  Google Scholar 

  47. Ali, S.N., et al.: Monkeypox skin lesion detection using deep learning models: a feasibility study. arXiv preprint http://arxiv.org/abs/2207.03342 (2022)

  48. Hashan, A.M., Agbozo, E., Al-Saeedi, A.A.K., Saha, S., Haidari, A., Rabi, M.N.F.: Brain tumor detection in MRI images using image processing techniques. In: 2021 4th International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR), pp. 24–28. IEEE (2021)

  49. Naseer, A., Yasir, T., Azhar, A., Shakeel, T., Zafar, K.: Computer-aided brain tumor diagnosis: performance evaluation of deep learner CNN using augmented brain MRI. Int. J. Biomed. Imaging 2021(1), 5513500 (2021)

    Google Scholar 

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Acknowledgements

The Key Research and Development Program of Xinjiang Autonomous Region (Grant 2021B01002), in part by the National Key Research and Development Program of China (Grant 2021ZD0140301), and in part by the National Natural Science Foundation of China (Project No. 61902433). This paper is supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 62302529), the Hunan Provincial Natural Science Foundation of China (2023JJ40770), the Changsha Municipal Natural Science Foundation (kq2208290).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China

    Saif Ur Rehman Khan, Sohaib Asif, Ming Zhao & Yangfan Li

  2. Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an, 710004, China

    Wei Zou

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  1. Saif Ur Rehman Khan
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Contributions

Saif Ur Rehman Khan: Conceptualization, Data curation, Methodology, Software, Validation, Writing original draft. Ming Zhao & Wei Zou: Conceptualization, Funding acquisition, Supervision. Sohaib Asif & Yangfan Li: Validation, Visualization, Writing – review & editing, Supervision, Validation, Formal analysis.

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Correspondence to Ming Zhao or Wei Zou.

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Communicated by Bing-kun Bao.

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Khan, S.U.R., Asif, S., Zhao, M. et al. Optimize brain tumor multiclass classification with manta ray foraging and improved residual block techniques. Multimedia Systems 31, 88 (2025). https://doi.org/10.1007/s00530-025-01670-3

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