Computer Aided Diagnosis of Melanoma Skin Cancer using Clinical Photographic Images

Abstract

Melanoma is considered as one of the most malignant, metastatic and dangerous form of skin cancer that may cause death. The curability and survival of this type of skin cancer depends directly on the diagnosis and removal of melanoma in its early stages. The accuracy of the clinical diagnosis of melanoma with the unaided eye is only about 60% depending only on the knowledge and experience that each doctor has accumulated. The need to the Computer-Aided Diagnosis system (CAD) is increased to be used as a non-invasive supporting tool for physicians as a second opinion to increase the accuracy of detection, as well contributing information about the essential optical characteristics for identifying them. The ultimate aim of this research is to design an automated low cost computer aided diagnosis system of melanoma skin cancer to increase system flexibility, availability. Also, investigate to what extent melanoma diagnosis can be impacted using clinical photographic images instead of using dermoscopic ones, regarding that both are applied upon the same automatic diagnosis system. Texture features was extracted from 140 pigmented skin lesion (PSL) based on Grey level Co-occurrence matrix (GLCM), effective features are selected by fisher score ranking and then classified using Artificial Neural Network (ANN), the whole system is processed through an interactive Graphical User Interface (GUI) to achieve simplicity. Results revealed the high performance of the proposed CAD system to discriminate melanoma from melanocytic skin tumors using texture analysis when applied on clinical photographic images with prediction accuracy of 100 % for the training phase and 91 % for the testing phase. Also, results indicated that using this type of images provides high prediction accuracy for melanoma diagnosis relevant to dermoscopic images considering that photographic clinical images are acquired using less expensive consumer which exhibit a certain degree of accuracy toward the edges of our field of view.

A high performance algorithm to diagnosis of skin lesions deterioration in dermatoscopic images using new feature extraction

“…Also, contrast measures are the coarseness of texture between pixel and its neighbor over the whole image, homogeneity measures are the distribution of element in the GLCM with respect to its diagonal, Correlation refers to pixel's neighborhood influence on the whole image surface, and energy measures are the image textural uniformity in the range [0, 1] [15].…”

Pigmented Skin Lesion Diagnosis by Automated Imaging System

“…Toward contributing information about the essential characteristics of pigmented skin lesion, in March 2012, Mariam A Sheha, Mai S Mabrouk, AmrSharawy proposed a Computer aided diagnosis system (CAD) that mainly concerned by analyzing only dermoscopic images based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma [10]. Moreover it was interesting to study the effect of this system using clinical images and compare results with dermoscopic images that was clearly represented on 2013 [11]. Other different features and classifiers were widely used in all fields of automated analysis and diagnosis that was valuable to introduce and study its effect.…”

Interpretable Aide Diagnosis System for Melanoma Recognition

“…Results show that their algorithm is able to classify malignant melanoma with 92% accuracy of the test set. In [13] the statistical textural features extraction derived from GLCM for classification of skin tumors are used. The results of this study are consistent with theory that using dermoscopic images is promising as it provides high accuracy rates.…”

“…The experiment uses 40 images containing suspicious melanoma skin cancer; the accuracy of the system reported is 92%. All these works [13][14][15] demonstrate that the separation of lesion from background is a critical early step in the analysis of dermatoscopic imagery. Fuzzy models have been widely and successfully used in many areas such as data mining [16] and image processing [17].…”