# Fuzzy Logic with Deep Learning for Detection of Skin Cancer
###### Abstract
A fully deep learning-based CAD method is employed by [PERSON] et al., where VGG net based fully connected network (FCN) is used for segmentation of lesion and a series of convolutional, pooling and fully connected layers are used to classification [18]. However, it has some limitation, FCN models down samples the input image multiple times to extract high-level features and generate segmentation maps. This down sampling reduces the spatial resolution of the segmentation output, making it challenging to accurately capture fine details and boundaries of small melanoma lesions. The loss of spatial information can affect the segmentation accuracy, especially when dealing with small or intricate lesions. [PERSON] et al. has used an intelligent fusion-based method for smart healthcare with 30 CNN layers [19]. Encapsulation of series of CNN layers tends to overfit the model and effect the speed of overall system thereby making it ineffective to be used as real-world application. InceptionNet is being employed by [PERSON] et al. extraction of patch based local features [20]. The InceptionNet architecture relies on convolutional operations of varying kernel sizes to capture information at different scales. However, it struggles to capture global contextual information and long-range dependencies that are important in melanoma classification. The lack of explicit modelling of spatial relationships and context does limit its ability to accurately classify melanoma lesions based on complex patterns and structures. Therefore, a robust and accurate deep learning-based CAD system is the need of the hour, which can not only capture the global contextual information but also able to classify the lesion without overfitting the model. This research paper utilizes a computer-vision approach to diagnose malignant lesions. The process involves three sequential steps: pre-processing of the lesion, segmentation of the pre-processed lesion image, and classification of the lesion. The algorithm's flow is visually represented in Figure 1 through a flowchart. In the initial pre-processing step, the image undergoes hair and artifact removal using the dull razor method, followed by histogram equalization. The novel segmentation algorithm proposed in the paper employs dynamic threshold calculation using standard deviation techniques and L-R fuzzy logic. For classification, a deep convolutional neural network called YOLO is utilized, which outperforms other CNN models [21]. Although the research relies on conventional methods of CAD system for melanoma lesion identification, its originality lies in the integration of modern aspects with well-established algorithms in malignant diagnosis. The proposed method offers two significant advantages: (i) the utilization of a publicly available deep learning-based neural network called YOLO, which accelerates the classification process and reduces the false detection error rate; and (ii) the introduction of a segmentation method based on standard deviation, which roughly separates the affected region from the surrounding skin. This algorithm operates independently on all three colour channels, and an ensemble logic is employed to combine the segmented marks. To further enhance the segmentation results, a second iterative procedure incorporating L-Function fuzzy number and improved defuzzification technique is introduced. The threshold series is adjusted using fuzzy theory to achieve better results compared to the first iteration. The L-Function fuzzy number is used to handle the indistinctness aspect, and an adapted threshold assessment rate is employed. These valuable algorithms significantly contribute to the detection of melanoma lesions [22; 23; 24]. The remaining sections of the paper are organized as follows: the next section provides a brief introduction to fuzzy logic in the context of the research, followed by a description of the dataset and the implementation of YOLO. Then, the proposed method is presented and supported by the results and analysis sections.