Artificial intelligence-assisted detection model for melanoma diagnosis using deep learning techniques

Year 2023, Volume: 3 Issue: 2, 159 - 169, 30.06.2023

Hediye Orhan , Emrehan Yavşan

https://doi.org/10.53391/mmnsa.1311943

Cited By: 5

Abstract

The progressive depletion of the ozone layer poses a significant threat to both human health and the environment. Prolonged exposure to ultraviolet radiation increases the risk of developing skin cancer, particularly melanoma. Early diagnosis and vigilant monitoring play a crucial role in the successful treatment of melanoma. Effective diagnostic strategies need to be implemented to curb the rising incidence of this disease worldwide. In this work, we propose an artificial intelligence-based detection model that employs deep learning techniques to accurately monitor nevi with characteristics that may indicate the presence of melanoma. A comprehensive dataset comprising 8598 images was utilized for the model development. The dataset underwent training, validation, and testing processes, employing the algorithms such as AlexNet, MobileNet, ResNet, VGG16, and VGG19, as documented in current literature. Among these algorithms, the MobileNet model demonstrated superior performance, achieving an accuracy of %84.94 after completing the training and testing phases. Future plans involve integrating this model with a desktop program compatible with various operating systems, thereby establishing a practical detection system. The proposed model has the potential to aid qualified healthcare professionals in the diagnosis of melanoma. Furthermore, we envision the development of a mobile application to facilitate melanoma detection in home environments, providing added convenience and accessibility.

Keywords

Artificial intelligence, deep learning, machine learning, melanoma detection, skin cancer

References

• Jain, S. and Pise, N. Computer aided melanoma skin cancer detection using image processing, Procedia Computer Science, 48, 735-740, (2015).
• Kaymak, R., Kaymak, C. and Ucar, A. Skin lesion segmentation using fully convolutional networks: A comparative experimental study. Expert Systems with Applications, 161, 113742, (2020).
• Tabrizchi, H., Parvizpour, S. and Razmara, J. An improved VGG model for skin cancer detection. Neural Processing Letters, 1-18, (2022).
• Kılıç, A.E. and Karakoyun, M. Breast cancer detection using machine learning algorithms. International Journal of Advanced Natural Sciences and Engineering Researches, 7(3), 91-95, (2023).
• Dildar, M., Akram, S., Irfan, M., Khan, H.U., Ramzan, M., Mahmood, A.R. et al. Skin cancer detection: a review using deep learning techniques. International Journal of Environmental Research and Public Health, 18(10), 5479, (2021).
• Skin Cancer Foundation. Melanoma. https://www.skincancer.org/skin-cancerinformation/melanoma/, 2021, Access Date: 13th April 2023.
• American Cancer Society. What Causes Melanoma Skin Cancer? https://www.cancer.org/cancer/melanoma-skin-cancer/causes-risks-prevention/what-causes.html, 2022, Access Date: 25th April 2023.
• Yildiz, O. Melanoma detection from dermoscopy images with deep learning methods: A comprehensive study. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4), 1-42, (2019).
• Sultana, N.N., Puhan, N.B. Recent deep learning methods for melanoma detection: a review. In Communications in Computer and Information Science (Mathematics and Computing) (vol. 834) pp. 118-132, Singapore: Springer, (2018).
• Poorna, S.S., Reddy, M.R.K., Akhil, N., Kamath, S., Mohan, L., Anuraj, K. and Pradeep, H.S. Computer vision aided study for melanoma detection: a deep learning versus conventional supervised learning approach. In Proceedings, Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2018 (Vol. 1) pp. 75-83, Singapore: Springer, (2020).
• Yavsan, E. and Ucar, A. Teaching human gestures to humanoid robots by using Kinect sensor. In Proceedings, 2015 23rd Signal Processing and Communications Applications Conference (SIU), pp. 1208-1211, Malatya, Turkey, (2015, May).
• Yavsan, E. and Ucar, A. Gesture imitation and recognition using Kinect sensor and extreme learning machines. Measurement, 94, 852-861, (2016).
• Baitu, G.P., Gadalla, O.A.A. and Öztekin, Y.B. Traditional machine learning-based classification of cashew kernels using colour features. Journal of Tekirdag Agricultural Faculty, 20(1), 115-124, (2023).
• Ucar, A. and Özalp, R. Efficient android electronic nose design for recognition and perception of fruit odors using Kernel Extreme Learning Machines. Chemometrics and Intelligent Laboratory Systems, 166, 69-80, (2017).
• Kwiatkowska, D., Kluska, P. and Reich, A. Convolutional neural networks for the detection of malignant melanoma in dermoscopy images. Advances in Dermatology and Allergology/Postepy Dermatol Alergol, 38(3), 412, (2021).
• Shchetinin, E.Y., Sevastianov, L.A., Kulyabov, D.S., Ayryan, E.A. and Demidova, A.V. Melanoma detection computer system development with deep neural networks. In Distributed Computer and Communication Networks: Control, Computation, Communications (Mathematics and Computing) (vol. 1337) pp. 422-434, Singapore: Springer, (2020).
• https://www.kaggle.com/datasets/mathewmarcum/nevusclassifier, 2020, Access Date: 22th February 2023.
• https://www.kaggle.com/datasets/wanderdust/skin-lesion-analysis-toward-melanoma-detection, 2020, Access Date: 20th February 2023.
• Altekin, F. and Demir, H. Emotion detection from facial expression using different feature descriptor methods with Convolutional Neural Networks. European Journal of Engineering and Applied Sciences, 4(1), 14-17, (2021).
• Efe, E. and Ozsen, S. CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets. Biomedical Signal Processing and Control, 80, 104299, (2023).
• Tajbakhsh, N., Roth, H., Terzopoulos, D. and Liang, J. Guest editorial annotation-efficient deep learning: the holy grail of medical imaging. IEEE transactions on medical imaging, 40(10), 2526-2533, (2021).
• Tatar, A.B. Biometric identification system using EEG signals. Neural Computing and Applications, 35(1), 1009-1023, (2023).
• Ucar, A. Deep Convolutional neural networks for facial expression recognition. In Proceedings, 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), pp. 371-375, Gdynia, Poland, (2017, July).
• Aslan, S.N., Özalp, R., Uçar, A. and Güzeliş, C. New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration. Cluster Computing, 25(3), 1575-1590, (2022).
• Küçük, Ö., Gökçe, B. and Yav¸san, E. Otonom tabanlı i¸saret ve ¸serit tanımak amacı ile bir ö˘grenme sisteminin geli¸stirilmesi. International Journal of Engineering Research and Development, 13(3), 19-25, (2021).
• Uçar, A., Demir, Y. and Güzeliş, C. Object recognition and detection with deep learning for autonomous driving applications. Simulation, 93(9), 759-769, (2017).
• Basha, S.H.S., Dubey, S.R., Pulabaigari, V. and Mukherjee, S. Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing, 378, 112-119, (2020).
• Polat, D.S. and Yavsan, E. Geri dönü¸sümdeki verimlili˘gi artırmak için yapay zeka destekli atık tespit sistemi. In Proceedings, 1st International Conference on Engineering, Natural and Social Sciences (ICENSOS 2022), pp. 757-761, Konya, Türkiye, (2022, December).
• Orhan, H. and Yavsan, E. A machine learning assisted monitoring system for early detection of melanoma. In Proceedings, 7th International Conference on Computational Mathematics and Engineering Sciences, pp. 110, Elazığ, Türkiye, (2023, May).
• Öztürk, P., Alisoy, H. and Mutlu, R. Prediction of CAT 6A U/FTP cable parameters produced by double twist and triple twist machines using artificial neural networks, and comparison of the predicted results. European Journal of Engineering and Applied Sciences, 2(2), 41-51, (2019).
• Kara, M.R. and Yavsan, E. Kapasitif bir algılayıcı üzerinden demodülasyon hatalarının yapay sinir ağları kullanılarak dü¸sürülmesi. In Proceedings, 1st International Conference on Engineering and Applied Natural Sciences (ICEANS’22), pp. 1705-1708, Konya, Türkiye, (2022, May).
• Reddy, R.V.K., Rao, B.S. and Raju, K.P. Handwritten Hindi digits recognition using convolutional neural network with RMSprop optimization. In Proceedings, 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 45-51, Madurai, India, (2018, June).
• Bock, S. and Weiß, M. A proof of local convergence for the Adam optimizer. In Proceedings, 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, Budapest, Hungary, (2019, July).
• Lydia, A. and Francis, S. Adagrad—an optimizer for stochastic gradient descent. International Journal of Information and Computing Science, 6(5), 566-568, (2018).
• Wang, Y., Liu, J., Mišic, J., Miši´c, V.B., Shaohua, L. and Chang, X. Assessing optimizer impact on DNN model sensitivity to adversarial examples. IEEE Access, 7, 152766-152776, (2019).
• Tato, A., Nkambou, R. Improving Adam optimizer. In Proceedings, ICLR 2018 Workshop, (2018).