International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 07 | July 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1366 Deep Learning-Based Skin Lesion Detection and Classification: A Review Niharika S 1 , Dr. Bhanushree K J 2 1 Department of Computer Science and Engineering, Bangalore Institute of Technology, Bengaluru, India 2 Assistant Professor, Department of Computer Science and Engineering, Bangalore Institute of Technology, Bengaluru, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Detection and classification of skin lesions are crucial in diagnosing skin cancer and detecting melanoma. Melanoma is a menacing form of skin cancer accountable for taking the lives of numerous people each year. Early identification of melanoma is essential and attainable through visual examination of pigmented lesions on the skin, treated by extirpating the cancerous cells. Standard vision detection of melanoma in skin lesion images might be imprecise. The visual similarity between the benign and malignant types poses hardship in identifying melanoma. To solve the problems in identifying melanoma, automated models are needed to assist dermatologists in the identification task. This paper presents a comprehensive review and analysis of the various deep learning techniques used to diagnose and classify skin lesions. Key Words: Skin cancer, skin lesion detection and classification, deep learning, image processing, Convolution Neural Network, Fuzzy neural network. 1. INTRODUCTION Skin lesions are skin portions with an atypical appearance or growth in contrast to the surrounding skin. Skin melanoma is a type of deadly skin cancer. The epidermis is one of the many layers of human skin, producing melanocytes that produce melanin at a high rate. Prolonged exposure to the sun's UV rays produces melanin. The abnormal development of melanocytes leads to melanoma, a cancerous tumour, the deadliest skin cancer. Early diagnosis of melanoma is essential for planning treatment and saving the affected. This is achievable by visual observation of pigmented skin lesions healed by simply removing the cancer cells. Detecting melanoma from images of skin lesions using human vision can be inaccurate. The stark resemblance between benign and malignant types poses hardship in differentiating between them and identifying melanoma. Also, traditional methods like biopsy are time-consuming, painful and expensive. Therefore, an automated computer model that supports specialists in identification tasks is essential. In recent times, deep learning techniques are frequently used skin lesion detection. It is considered a class of machine learning that utilises several layers to extricate complex- level features from the input. Since a considerable amount of research has been done regarding skin lesion detection using deep learning techniques. It's vital to survey and summarise the research findings for future researchers. This paper reviews the different deep learning techniques used, like convolution neural networks and artificial neural networks for skin lesion detection. 2. LITERATURE REVIEW M. Kahn et al. [1] proposed a fully automated system classifying skin lesions into many classes. They describe segmentation techniques using deep learning and CNN feature optimisation using an enhanced Moth Flame Optimization (IMFO) method as part of the framework. First, the input image is stretched with the Histogram Intensity Value with Local Color Key (LCcHIV). Subsequently, saliency is evaluated using a new deep saliency segmentation technique using a 10-layer convolutional neural network. A pre-trained CNN is used for feature extraction from the segmented colour lesion images. They proposed an improved Moth Flame Optimization (IMFO) algorithm to choose the most discriminating features. The Kernel Extreme Learning Machine (KELM) classifies the features. The limitation of this task is the increase in calculation time. In addition, advanced segmentation techniques are needed to avoid deep model training on irrelevant image features. P. Dhar et al. [2] put forward a technique for segmentation and detecting skin lesions utilising dermoscopy images. The proposed method is based on fuzzy logic and classification rules using CNN. First, a set of rules is adapted to the dermoscopy image. The output is thresholded. The close operation is used as a morphological tool on threshold images. Area filtering is then performed to generate the desired area. For classification, CNN was used. The dataset under consideration is inadequate and unbalanced. Classification of images without skin lesions gave poor results. M. Arshad et al. [3] presented a novel automated framework for classifying multiclass skin lesions. The pre-processing involves three operations: 90 rotations, flip left / right and flip-up / down. Next, the deep model is fine-tuned. ResNet50 and ResNet101 are the two selected models, and their layers are updated. In addition, transfer learning is applied, features are extricated, and fusion is performed using an altered series-based method. The final selected feature is categorised using multiple machine learning algorithms. The fusion system's limitations include an increase in