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A Probabilistic Neural Network Framework for the Detection of Malignant Melanoma
Published in Raouf N.G. Naguib, Gajanan V. Sherbet, Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management, 2001
M. Hintz-Madsen, L.K. Hansen, J. Larsen, K.T. Drzewiecki
Malignant melanoma is the deadliest form of skin cancer and arises from cancerous growth in pigmented skin lesions. The cancer can be removed by a fairly simple surgical incision if it has not entered the blood stream. It is thus vital that the cancer is detected at an early stage in order to increase the probability of a complete recovery. Skin lesions may in this context be grouped into three classes: Benign nevi is a common name for all healthy skin lesions. These have no increased risk of developing cancer.Atypical nevi are also healthy skin lesions but have an increased risk of developing into cancerous lesions. The special type of atypical nevi, called dysplastic nevi, has the highest risk and is, thus, often referred to as the precursor of malignant melanoma.Malignant melanoma are as already mentioned cancerous skin lesions.
Convolutional Neural Network for Classification of Skin Cancer Images
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
Giang Son Tran, Quoc Viet Kieu, Thi Phuong Nghiem
Dermoscopy images are widely used by dermatologists to examine suspicious skin lesions [3]. To do so, the doctor usually looks at the dermoscopic images of skin lesions to determine if there exists cancer and, if so, which type of cancer the lesion may represent. Nevertheless, one problem that arises is that to analyze these million images of skin cancer is a massive workload for dermatologists due to the high variance of size, shape, texture, location between the healthy skin and the damaged skin.
CAD of Dermatological Ulcers (Computational Aspects of CAD for Image Analysis of Foot and Leg Dermatological Lesions)
Published in de Azevedo-Marques Paulo Mazzoncini, Mencattini Arianna, Salmeri Marcello, Rangayyan Rangaraj M., Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy, 2018
Marco Andrey Cipriani Frade, Guilherme Ferreira Caetano, Éderson Dorileo
Diagnosis of skin lesions in dermatology are based mainly on visual assessment of pathological regions and the evaluation of macroscopic features. This fact indicates that correct diagnosis is highly dependent on the observer’s experience and visual perception [13]. In order to help clinicians making decisions on diagnostics, some tools have been used to assist them to treat different wound types and also to work on clinical images.
MSRFNet for skin lesion segmentation and deep learning with hybrid optimization for skin cancer detection
Published in The Imaging Science Journal, 2023
Diwan Baskaran, Yanda Nagamani, Suneetha Merugula, S P Premnath
Cancers of the skin are a familiar type of malignancy in the human system. Specialists of skin cancer inspect skin lesions of patients by visual inspection controlled by hand held dermoscopy. They capture macroscopic or digital close-up images, and microscopic or dermoscopic images [14]. There are four categories of cancers in the skin, such as Melanoma, Squamous cell carcinoma (SCC), Actinic Keratoses (AK), and Basal cell carcinoma (BCC) [15,16]. Earlier diagnosis of cancer helps in treating the disease successfully. Late diagnosis of cancer causes cancer to spread to various other nearby organs, and it cannot be well treated [17]. Skin lesions are normally grouped into the non-melanocytic type and melanocytic type. Melanocytic lesions include melanocytic nevi and melanoma. Conversely, non-melanocytic lesions include cactinic keratosis which is the earliest form of squamous cell carcinoma, which is also called Bowen’s disease, basal cell carcinoma, vascular lesion, and dermatofibroma. Both non-melanocytic and melanocytic lesions have malignant and benign categories of disease. Malignant skin lesions are cancerous that include melanoma, actinic keratosis, and basal cell carcinoma [18]. The computer-aided task is significant in classifying skin lesions, mainly melanoma using dermoscopy images. Classification of skin lesions is much more complicated to classify scenes or objects in natural images [19]. Computer-aided skin lesion classification and detection has various steps such as pre-processing phase, segmentation stage, extraction of features, and finally classification. Major challenges in computer-aided detection systems are the segmentation of lesion area and the selection of prominent features from raw data [20,21]. This is avoided by finding brightness and illumination of the lesion, smooth alteration among lesion and skin area, accurate extraction of features, and prominent selection of features [22].