Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms

• Published in: Journal of medical systems Vol. 40; no. 4; p. 96
• Main Authors: Premaladha, J., Ravichandran, K. S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2016; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Dermoscopy - methods; Diagnosis, Computer-Assisted - methods; Early Detection of Cancer; Health Informatics; Health Sciences; Humans; Image Interpretation, Computer-Assisted; Male; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Melanoma; Melanoma - diagnosis; Melanoma - pathology; Neural Networks (Computer); Skin Neoplasms - diagnosis; Skin Neoplasms - pathology; Statistics for Life Sciences; Support Vector Machine; Systems-Level Quality Improvement; Deep learning; Preprocessing; Segmentation; Classification; Artificial neural networks; Support vector machine; Adaboost
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-016-0460-2

Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter . A new segmentation algorithm called Normalized Otsu’s Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.