# Fuzzy Logic with Deep Learning for Detection of Skin Cancer
###### Abstract  
Melanoma is the deadliest type of cancerous cell, which is developed when melanocytes, melanin producing cell, starts its uncontrolled growth. If not detected and cured in its situ, it might decrease the chances of survival of patients. The diagnosis of a melanoma lesion is still a challenging task due to its visual similarities with benign lesions. 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 image enhancement using pre-processing techniques, infusion of mathematical logics, standard deviation methods, and the L-R fuzzy defuzzification method to enhance the results of segmentation. These pre-processing steps are developed to improve the visibility of lesion by removing artefacts such as hair follicles, dermoscopic scales, etc. Thereafter, the image is enhanced by histogram equalization method, and it is segmented by proposed method prior to performing the detection phase. The modified model employs a deep neural network algorithm, You Look Only Once (YOLO), which is established on the application of Deep convolutional neural network (DCNN) for detection of melanoma lesion from digital and dermoscopic lesion images. The YOLO model is composed of a series of DCNN layers we have added more depth by adding convolutional layer and residual connections. Moreover, we have introduced feature concatenation at different layers which combines multi-scale features. Our experimental results confirm that YOLO provides a better accuracy score and is faster than most of the pre-existing classifiers. The classifier is trained with 2000 and 8695 dermoscopic images from ISIC 2017 and ISIC 2018 datasets, whereas PH2 datasets along with both the previously mentioned datasets are used for testing the proposed algorithm. skin cancer; melanoma; YOLO; fuzzy logic; melanoma detection Article  
Fuzzy Logic with Deep Learning for Detection of Skin Cancer  
202313892  
data, 63,000 melanoma lesions were diagnosed in the USA from 2007 to 2011. According to the American cancer society's (ACS) 2017 report, every 54-60 min a person passes away due to melanoma [5]. ACS's annual report of 2019 estimates 96,480 new melanoma cases with 7230 deaths from melanoma [6]. However, melanoma can be cured if diagnosed and detected at an early stage [7]. The survival rate of melanoma is 95% when diagnosed in situ (Stage 0), whereas the rate decreases below 15% if diagnosed in an advance stage [8], if the tumor is not treated at its early state, it might metastases to lung or liver tumor, thereby decreasing the mortality rate to less than 20% even after surgery. The report draws a clear conclusion that timely identification and treatment of melanocytic lesion is directly proportional to survival rate. Thereby, identification of melanoma lesions in its premature phase is imperative (although the nature and intense growth of the lesion makes this a formidable task). As stated by dermatologists, the melanoma lesion can be visually similar to a mole, pigmentation or/and non-melanocytic lesions, which makes it difficult to detect. Malignant lesions are irregular and ameboid surfaced, rapidly growing lump, generally more than 5 mm in dimension, asymmetrical in shape, and can appear in shades of brown, black, or deep gray. Lesion visually appears such as a contour of various color pigmented as a cluster. Rapid development of the melanoma lesion may lead to itching, inflammation, ulceration, or even bleeding. However, dermatologists also agree that a melanoma lesion might not show any systematic sign and can grow in any area irrespective of exposure to sun's direct radiation. The dawn of twentieth century has witnessed traditional methods of diagnosis and identification of skin cancer by physical screening and visual examination of lesions. Clinical inspection of skin cancer by dermatologists depends on visual changes in size, shape, and/or color.