Abstract
Skin cancer presents a significant public health concern, necessitating early detection and intervention to mitigate life-threatening consequences. Despite its prevalence, skin cancer is not always given the attention it deserves. In this paper, we propose an automated procedure for distinguishing skin cancers using several neural network algorithms. We used the MobileNet architecture whose accuracy is not very high and this is why we have also used other algorithms such as Sequential CNN (Convolution Neural Network), VGG16, and Xception to improve the accuracy of our model. By comparing these algorithms, we found that the Sequential CNN performed best with an accuracy of 94%. The proposed model utilizes the Sequential CNN algorithm to achieve a high level of accuracy, which is of utmost importance for the early detection of skin cancer. The high accuracy of the proposed model makes it a promising tool for detecting skin cancer early.
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References
Victor A, Ghalib MR (2017) Automatic detection and classification of skin cancer. Int J Intell Eng Syst 10(3):444–451. https://doi.org/10.22266/ijies2017.0630.50
Basak H, Kundu R, Sarkar R (2022) MFSNet: a multi focus segmentation network for skin lesion segmentation. https://doi.org/10.1016/j.patcog.2022.108673
Arif M, Philip FM, Ajesh F, Izdrui D, Craciun MD, Geman O (2022) Automated detection of nonmelanoma skin cancer based on deep convolutional neural network. J Healthc Eng. https://doi.org/10.1155/2022/6952304
Brahmbhatt P, Nath Rajan S (2019) Skin lesion segmentation using segnet with binary cross-entropy
Muzahidul Islam Rahi M, Amanat Ullah AKM, Tanvir Khan F, Golam Rabiul Alam M, Tanvir Mahtab M, Ashraful Alam M (2019) Detectıon of skın cancer usıng deep neural networks
Dildar M et al (2021) Skin cancer detection: a review using deep learning techniques. Int J Environ Res Public Health 18(10). https://doi.org/10.3390/ijerph18105479
Khan MQ et al (2019) Classification of melanoma and nevus in digital images for diagnosis of skin cancer. IEEE Access 7:90132–90144. https://doi.org/10.1109/ACCESS.2019.2926837
Taufiq MA, Hameed N, Anjum A, Hameed F (2017) m-skin doctor: a mobile enabled system for early melanoma skin cancer detection using support vector machine. İn lecture notes of the ınstitute for computer sciences, social-ınformatics and telecommunications engineering, LNICST, 2017, vol 181 LNICST, pp 468–475. https://doi.org/10.1007/978-3-319-49655-9_57
Sigurdsson S, Philipsen PA, Hansen LK, Larsen J, Gniadecka M, Christian Wulf H (2004) Detection of skin cancer by classification of Raman spectra. IEEE Trans Biomed Eng 51(10):1784–1793. https://doi.org/10.1109/TBME.2004.831538
Goyal M, Knackstedt T, Yan S, Hassanpour S (2020) Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities. Comput Biol Med 127. Elsevier Ltd. https://doi.org/10.1016/j.compbiomed.2020.104065
Andrey K, Skin cancer ISIC the ınternational skin ımaging collaboration
Sinha D, El-Sharkawy M (2019) Thin mobilenet: an enhanced mobilenet architecture. In: 2019 IEEE 10th annual ubiquitous computing, electronics and mobile communication conference (UEMCON), New York, NY, USA, pp 0280–0285. https://doi.org/10.1109/UEMCON47517.2019.8993089
Hasan M, Barman SD, Islam S, Reza AW (2019) Skin cancer detection using convolutional neural network. In: Proceedings of the 2019 5th international conference on computing and artificial intelligence, pp 254–258
Chauhan R, Ghanshala KK, Joshi RC (2018) Convolutional Neural Network (CNN) for ımage detection and recognition. In: 2018 first ınternational conference on secure cyber computing and communication (ICSCCC), Jalandhar, India, pp 278–282. https://doi.org/10.1109/ICSCCC.2018.8703316
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195
Liu S, Deng W (2015) Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian conference on pattern recognition (ACPR), Kuala Lumpur, Malaysia, pp 730–734. https://doi.org/10.1109/ACPR.2015.7486599
Moussa R, Gerges F, Salem C, Akiki R, Falou O, Azar D (2016) Computer-aided detection of Melanoma using geometric features. Middle East MECBME, pp 125–128
Farooq MA, Azhar MAM, Raza RH (2016) Automatic lesion detection system (ALDS) for skin cancer classification using svm and neural classifiers. In: Proceedings 2016 IEEE 16th ınternational conference on bioinformatics and bioengineering BIBE 2016, pp 301–308
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Hossain, M.J. et al. (2024). Enhancing Skin Cancer Detection Through Automated Classification Using Convolutional Neural Networks. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 1167. Springer, Cham. https://doi.org/10.1007/978-3-031-73318-5_10
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DOI: https://doi.org/10.1007/978-3-031-73318-5_10
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