# Fuzzy Logic with Deep Learning for Detection of Skin Cancer
###### Abstract
These conventional approaches are complex, susceptible, and time consuming due to the visual complexities of skin lesions. Physical inspection demands a proficient specialist for accurate detection of the lesion. However, the reports for a particular lesion varies for different dermatologist due to inter observation variation. Thus, the accuracy of lesion detection ranges between 74% and 85% when diagnosed by an untrained dermatologist [9]. Thereafter, the process of manual diagnosis was replaced with radioscopy, immunotherapy and invasive methods such as anatomical pathology, which is a surgical incision for detection of malignant tumor wherein skin tissues are incised and examined by a pathologist. Invasive methods are not preferred to be practiced by clinical patients as these are painful and might take 3-12 weeks. Thus, with passing years, non-invasive methods have gained significance, and modern economical equipment such as Epiluminescence microscopy and dermoscopy are employed more often which produces superior accuracy over previous methods [10]. Various in state-of-the-art (SOTA) noninvasive diagnosis techniques have been described such as digital photography, dermatoscopy, multispectral imaging, optical coherence tomography (OCT), magnetic resonance imaging (MRI), confocal laser scanning-microscopy, laser-based systems, ultrasound, conventional microscope, and multiphoton tomography. However, dermatoscopy is much efficient device for lesion detection. Dermoscopic devices are used to magnify and illuminate the images of effected skin region, thereby increasing the clarity of the diagnosed pigment and reducing reflection on the skin surface. Diagnosis performances are improved significantly with the use of these modern dermoscopy devices. Visual inspection is standardized and modernized with the help of contemporary methods and algorithms such as CASH (color, architecture, symmetry, homogeneity), ABCDE rule (asymmetry, border, color, diameter, evolving), Menzies method, and Seven-point checklist [11; 12; 13; 14]. Although dermatologists use modern dermoscopy devices and visual inspection methods are unable to gain high accuracy marks due to low contrast images between skin and lesion which makes it difficult to differentiate a melanoma from non-melanoma skin lesion. In addition, various skin conditions such as hair, blood vessels, skin tone, and the presence of air bubbles and ruler marks in the dermoscopy image makes it challenging for a dermatologist to detect the lesion accurately. Thus, the processes of identification of a lesion can become slow and time consuming for a dermatologist. Therefore, our predominant goal should be the development of a swift, efficient, and accurate method for diagnosis of malignant lesion in its early stage. Thus, a noninvasive computer aided diagnosis (CAD) system must be considered for regular practice, which will not only relieve the patients from complication of invasive pathology but also increase the accuracy and speed of diagnosis [15]. Advancement in machine learning algorithms and segmentation techniques has ameliorated CAD systems and classification of deadly skin cancerous lesion over past decade. CAD systems have shifted from workstations and desktops to smart phones and enables the pathologist to detect malignant lesion which are not visible to human eyes [16]. [PERSON] et al. has proposed a deep learning method for digital diagnosis of melanoma where the lesion is segmented using neutrosophic logic and convolutional neural network (CNN) is used for classification [17]. The CNN based architecture is not efficient as it primarily focuses on local features within repetitive field of each convolutional layer, therefore it does not capture enough contextual information. Moreover, the CNN models are sensitive to variations such as the presence of hair follicle or other artifacts along with noise in the image.