… In this paper, a fuzzy logic-based image segmentation along with a modified deep learning
model is proposed for skin cancer detection. The highlight of the paper is its dermoscopic …

… It becomes necessary to diagnose this skin malignancy at an early … skin cancer dataset.
In this paper, a hybrid technique was utilized that combined the advantages of the fuzzy logic …

… The fuzzy logic inference system used in this research was intended to make the
differentiation results between skin cancer types more accurate. It is based on the nature of fuzzy …

… kinds of cancer diseases early such as skin cancer, and others. In this paper two techniques
have been used to detect Skin Cancer. These two techniques are Fuzzy logic and GLCM (…

… " Skin cancer, the most common cancer in the United States that affects about 600,000 Americans
every year, accounts for 1% of all cancer … skin cancer, especially malignant melanoma. …

… cell carcinoma (SCC) is a type of skin malignancy which are deadly in nature. Although both
… data of skin cancer. In this research, the same is spotted out with the help of the fuzzy logic …

… of skin cancer is higher than that of all other cancers … , skin cancer accounts for one third of
all types of cancers happening … The three most commonly reported skin cancers are basal cell …

… types of skin cancers using fuzzy logic approach. The traditional skin cancer segmentation
involves the analysis of image features to delineate the cancerous region from the normal skin…

… Then, the second element comprises fuzzy logic modules … fuzzy rules 5, 6, and 7 (ie, FR5,
FR6, FR7). For the generation of fuzzy rules, we use the ABCD framework [49] for skin cancer …

… on neural networks and fuzzy logic. Simulation results of the study indicated 96.04% accuracy
for the artificial neural networks and 76% correct classification for the fuzzy logic system. …