Skin lesion detection using deep learning

• Autorzy: Chandra Rajit , Hajiarbabi Mohammadreza

Identyfikatory

DOI

10.14313/JAMRIS/3-2022/24

• Języki publikacji: EN

Abstrakty

EN

Skin lesion can be deadliest if not detected early. Early detection of skin lesion can save many lives. Artificial Intelligence and Machine learning is helping healthcare in many ways and so in the diagnosis of skin lesion. Computer aided diagnosis help clinicians in detecting the cancer. The study was conducted to classify the seven classes of skin lesion using very powerful convolutional neural networks. The two pre trained models i.e DenseNet and Incepton-v3 were employed to train the model and accuracy, precision, recall, f1score and ROCAUC was calculated for every class prediction. Moreover, gradient class activation maps were also used to aid the clinicians in determining what are the regions of image that influence model to make a certain decision. These visualizations are used for explain ability of the model. Experiments showed that DenseNet performed better then Inception V3. Also it was noted that gradient class activation maps highlighted different regions for predicting same class. The main contribution was to introduce medical aided visualizations in lesion classification model that will help clinicians in understanding the decisions of the model. It will enhance the reliability of the model. Also, different optimizers were employed with both models to compare the accuracies.

Słowa kluczowe

EN

skin lesion; DenseNet; Inception V3

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 3
• Strony: 56--64
• Opis fizyczny: Biubliogr. 7 poz., rys.

Twórcy

autor

Chandra Rajit

• Computer Science Department, Purdue Fort Wayne, Fort Wayne, 46805, USA

autor

Hajiarbabi Mohammadreza

• Computer Science Department, Purdue Fort Wayne, Fort Wayne, 46805, USA

Bibliografia

• [1] N. Gessert, M. Nielsen, M. Shaikh, R. Werner, and A. Schlaefer, “Skin sion classification using ensembles of multi-resolution EfficientNets with meta data,” MethodsX, vol. 7, p. 100864, 2020, DOI: 10.1016/j.mex.2020.100864.
• [2] J. Yap, W. Yolland, and P. Tschandl, “Multimodal skin lesion classification using deep learning,” Exp. Dermatol., vol. 27, no. 11, pp. 1261–1267, 2018, DOI: 10.1111/exd. 13777.
• [3] P. Mirunalini, A. Chandrabose, V. Gokul, and S. M. Jaisakthi, “Deep Learning for Skin Lesion Classification,” 2017, [Online]. Available: http://arxiv.org/abs/1703.04364.
• [4] T. C. Pham, C. M. Luong, M. Visani, and V. D. Hoang, “Deep CNN and Data Augmentation for Skin Lesion Classification,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10752 LNAI, no. June, pp. 573–582, 2018, DOI: 10.1007/978-3-319-75420-8_54.
• [5] T. Majtner, S. Yildirim-Yayilgan, and J. Y. Hardeberg, “Combining deep learning and hand-crafted features for skin lesion classification,” 2016 6th Int. Conf. Image Process. Theory, Tools Appl. IPTA 2016, no. December, 2017, DOI: 10.1109/IPTA.2016.7821017.
• [6] A. Mahbod, G. Schaefer, I. Ellinger, R. Ecker, A. Pitiot, and C. Wang, “Fusing fine-tuned deep features for skin lesion classification,” Comput. Med. Imaging Graph., vol. 71, pp. 19–29, 2019, DOI: 10.1016/j.compmedimag.2018.10.007.
• [7] J. Zhang, Y. Xie, Y. Xia, and C. Shen, “Attention Residual Learning for Skin Lesion Classification,” IEEE Trans. Med. Imaging, vol. 38, no. 9, pp. 2092–2103, 2019, doi: 10.1109/TMI.2019.2893944.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).