Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.

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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.

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Copy number changes or alterations are a form of genetic variation in the human genome. Genomic DNA copy number alterations (CNAs) are associated with the development and progression of cancers. Array-based comparative genomic hybridization (a-CGH) is a technique used to identify copy number changes in genomic DNA. It yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of automated statistics algorithms for learning about the genomic alterations from array CGH data. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data. For this purpose the proposed study introduces three different approaches; Circular binary segmentation, Bayesian approach, relying on the hidden Markov model and effective Gaussian mixture (GM) clustering for the analysis of array CGH profiles. Publicly available data on pancreatic adenocarcinoma and Coriell cell line bacterial artificial chromosome (BAC) array were used for the analysis to illustrate the reliability and success of the techniques.