Diagnosis of Malignant Melanoma of Skin Cancer Types

Malignant melanoma is a kind of skin cancer that begins in melanocytes. It can influence on the skin only, or it may expand to the bones and organs. It is less common, but more serious and aggressive than other types of skin cancer. Malignant Melanoma can happen anywhere on the skin, but it is widespread in certain locations such as the legs in women, the back and chest in men, the face, the neck, mouth, eyes, and genitals. In this paper, a proposed algorithm is designed for diagnosing malignant melanoma types by using digital image processing techniques. The algorithm consists of four steps: preprocessing, separation, features extraction, and diagnosis. A neural network (NN) used to diagnosis malignant melanoma types. The total accuracy of the neural network was 100% for training and 93% for testing. The evaluation of the algorithm is done by using sensitivity, specificity, and accuracy. The sensitivity of NN in diagnosing malignant melanoma types was 95.6%, while the specificity was 92.2% and the accuracy was 93.9%. The experimental results are acceptable

Deep CNN Based Skin Lesion Image Denoising and Segmentation using Active Contour Method

Automatic skin lesion segmentation on skin images is an essential component in diagnosing skin cancer. Image de-noising in skin cancer lesion is a description of processing image which refers to image restoration techniques to develop an image in predefined touch. Then de-noising is the crucial step of image processing to restore the right quality image after that which can use in many processes like segmentation, detection. This work proposes a new technique for skin lesion tumor denoising and segmentation. Initially, using Deep Convolution Neural Network (CNN) to eliminate noise and undesired structures for the images. Then, a new mechanism is proposed to segment the skin lesion into skin images based on active_contour straight with morphological processes. Different noise removal and segmentation techniques on skin lesion images are applying and comparing. The proposed algorithm shows improvement in the results of both noise reduction and segmentatio

Diagnosis of Malignant Melanoma of Skin Cancer Types

Malignant melanoma is a kind of skin cancer that begins in melanocytes. It can influence on the skin only, or it may expand to the bones and organs. It is less common, but more serious and aggressive than other types of skin cancer. Malignant Melanoma can happen anywhere on the skin, but it is widespread in certain locations such as the legs in women, the back and chest in men, the face, the neck, mouth, eyes, and genitals. In this paper, a proposed algorithm is designed for diagnosing malignant melanoma types by using digital image processing techniques. The algorithm consists of four steps: preprocessing, separation, features extraction, and diagnosis. A neural network (NN) used to diagnosis malignant melanoma types. The total accuracy of the neural network was 100% for training and 93% for testing. The evaluation of the algorithm is done by using sensitivity, specificity, and accuracy. The sensitivity of NN in diagnosing malignant melanoma types was 95.6%, while the specificity was 92.2% and the accuracy was 93.9%. The experimental results are acceptable