Author : Ms. Prajakta P. Shirke 1
Date of Publication :28th February 2022
Abstract: Melanoma, or skin cancer, is usually detected visually from dermoscopic pictures, which is a time-consuming and difficult job for the dermatologist. Existing systems either utilize classic machine learning models that concentrate on hand-picked acceptable features, or deep learning-based approaches that learn features from full pictures. Melanoma, or skin cancer, is usually detected visually from dermoscopic pictures, which is a time-consuming and difficult job for the dermatologist. Due to many artifacts such as poor contrast, diverse noise, presence of hair, fiber, and air bubbles, etc., such a visual examination with the naked eye for skin malignancies is tough and onerous. This paper provides a robust and automated system for Skin Lesion Classification (SLC), in which image augmentation, Deep Convolutional Neural Network (DCNN), and transfer learning are all combined. Our lesion classification experiments show that the suggested technique can effectively classify skin cancer with a high degree of accuracy, and that it can also identify skin lesions for melanoma detection
Reference :
-
- Md. Kamrul Hasan, Md. Toufick E Elahi, Md. Ashraful Alam (2021) ―DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation‖ medRxiv 2021.02.02.21251038; doi:
- Zheng, Jing & Lin, Denan & Gao, Zhongjun & Wang, Shuang & He, Mingjie & Fan, Jipeng. (2020). Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2993536.
- Harangi, B.; Baran, A.; Hajdu, A. Assisted deep learning framework for multi-class skin lesion classification considering a binaryclassification support.Biomed. Signal Process. Control2020,62, 102041
- Gulati, Savy & Bhogal, Rosepreet. (2019). Detection of Malignant Melanoma Using Deep Learning. 10.1007/978-981-13-9939-8_28.
- Adegun, Adekanmi & Viriri, Serestina. (2019). Deep Learning-Based System for Automatic Melanoma Detection. IEEE Access. PP. 1-1. 10.1109/ACCESS.2019.2962812.
- Moldovan, Dorin. (2019). Transfer Learning Based Method for Two-Step Skin Cancer Images Classification. 10.1109/EHB47216.2019.8970067.
- Nida, Nudrat & Irtaza, Aun & Javed, Ali & Yousaf, Muhammad Haroon & Mahmood, Muhammad. (2019). Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics. 124. 10.1016/j.ijmedinf.2019.01.005.
- Tschandl, P.; Rosendahl, C.; Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images ofcommon pigmented skin lesions.Sci. Data2018,5.
- Farooq, Muhammad & Azhar, Muhammad & Raza, Rana. (2016). Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers. 301-308. 10.1109/BIBE.2016.53.
- Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper withconvolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA,USA, 7–15 June 2015.
- Bruno, D.O.T., et al., LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues. Expert Systems with Applications, 2016. 55: p. 329-340.
- Abbasi, N.R., et al., Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria. Jama, 2004. 292(22): p. 2771-2776.
- Johr, R.H., Dermoscopy: alternative melanocytic algorithms—the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist. Clinics in dermatology, 2002. 20(3): p. 240-247.
- Kittler, H., et al., Morphologic changes of pigmented skin lesions: a useful extension of the ABCD rule for dermatoscopy. Journal of the American Academy of Dermatology, 1999. 40(4):p. 558-562.
- Almansour, E. and M.A. Jaffar, Classification of Dermoscopic skin cancer images using color and hybrid texture features. IJCSNS Int J Comput Sci Netw Secur, 2016. 16(4): p. 135-9.
- Xie, F., et al., Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE transactions on medical imaging, 2016. 36(3): p. 849-858.