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.
Implementation of Computer Aided Diagnosis System for Lung Cancer Detection
