Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification

Abstract

Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized dermoscopy system that considers the estimated depth of skin lesions for diagnosis. A 3-D skin lesion reconstruction technique using the estimated depth obtained from regular dermoscopic images is presented. On basis of the 3-D reconstruction, depth and 3-D shape features are extracted. In addition to 3-D features, regular color, texture, and 2-D shape features are also extracted. Feature extraction is critical to achieve accurate results. Apart from melanoma, in-situ melanoma the proposed system is designed to diagnose basal cell carcinoma, blue nevus, dermatofibroma, haemangioma, seborrhoeic keratosis, and normal mole lesions. For experimental evaluations, the PH2, ISIC: Melanoma Project, and ATLAS dermoscopy data sets is considered. Different feature set combinations is considered and performance is evaluated. Significant performance improvement is reported the post inclusion of estimated depth and 3-D features. The good classification scores of sensitivity = 96%, specificity = 97% on PH2 data set and sensitivity = 98%, specificity = 99% on the ATLAS data set is achieved. Experiments conducted to estimate tumor depth from 3-D lesion reconstruction is presented. Experimental results achieved prove that the proposed computerized dermoscopy system is efficient and can be used to diagnose varied skin lesion dermoscopy images.

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

“…To follow a few well‐known methods, we compare our proposed classification accuracy with these performance parameters. Satheesha et al () introduced a computerized method for skin lesions recognition and evaluated on the PH2 dataset with achieved accuracy of 97%. The more recent, Akram et al () presented a saliency and traditional features‐based approach for melanoma recognition and achieved an accuracy of 97.50%.…”

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

“…Also, the implementation of histogram equalization as a preprocessing step improved the U‐Net segmentation accuracy. Satheesha, Satyanarayana, Prasad, and Dhruve () presented a noninvasive 3D reconstruction method for skin lesion classification. The 3D shape and depth features are extracted from the 3D reconstruction method in addition to color, shape, and texture features.…”

Classification with Stochastic Learning Methods and Convolutional Neural Networks

“…If melanoma is detected at an early stage, it is curable. Otherwise, the cost of treatment after a late detection is quite high and may reach about $134,000 in its IV stage [1,2]. Automated methods are seen as a methodology which removes the inherent bias introduced by visual examinations and can be utilized as a third-party opinion in the medical field [3].…”