# Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances
## 2 Literature Review  
Diseases of the skin are one of the most widespread human illnesses. It can have detrimental effects on human skin and health as a whole. Whether it is a small blemish or a major tear, it can lead to the development of dangerous or malignant forms, which can be fatal. In spite of these repercussions, the majority of people believe this illness to be trivial. As skin disorders have become a huge worldwide burden, there is an urgent need to bring them under control. In relation to the number of patients, the number of dermatologists (who treat skin disorders) is incredibly low [21]. [PERSON] [22] selects five separate machine learning approaches that may be used to various datasets of skin illnesses in order to determine the particular type of skin ailment. These machine learning algorithms were able to identify between lichen planus, sjs-ten, and acne, three different forms of skin problems. The dataset was first partitioned and preprocessed. In total, 80% of the data was employed for the training dataset and 20% for the test dataset. For the aforementioned three diseases, the test dataset was separated into three unique groups, namely Class A, Class B, and Class C. Five machine learning algorithms, including Kernel Support Vector Machines, Naive Bayes, Random Forest, convolutional neural network (CNN), and logistic regression, were then employed to detect skin disorders. Ten iterations of each method were performed on the same dataset, and the outcomes were compared. CNN's training accuracy of 99.05% and testing accuracy of 96% were considered to be the highest following a comparison. [PERSON] utilized an additional CNN approach released in [23] to develop an artificial intelligence dermatological diagnosis helper (AIDDA). In this method, a CNN model was trained using a dataset of 4740 clinical pictures, and its performance was assessed using clinical images verified by specialists and categorized according to three separate diagnoses. The source of the photos in the collection is \"The Second Xiangya Hospital, Central South University, China.\" Using five-fold cross validation, the algorithm's effectiveness was confirmed. Then, 20% of the cases were used for validation, and all images used for validation and training were examined by dermatological professionals. A framework for deep learning for the network was developed using Pytorch. The resultant model was able to categorize every image into one of three categories. The model's accuracy and sensitivity are 95.60% and 94.40%, respectively. There are several diseases that might lead to skin cancer. Malignant melanoma is the deadliest of these cancers. It might be dangerous if it goes undetected or spreads across the skin. Standard symptoms include an asymmetrical area, color variation, and a diameter more than 6 mm. [PERSON] and [PERSON] created an Artificial Neural Network (ANN) model for the categorization of melanoma images, which is beneficial for diagnosing skin cancer caused by malignant melanoma [24]. Images were preprocessed before to segmentation in this model. A modified version of the Sobel edge detection technique was implemented for segmentation. After segmentation, desired attributes were extracted from segmented images and selected. These characteristics were then subdivided into n sub-feature spaces, and these sub-features were ultimately employed for classification with an ANN. Four various types of algorithms were evaluated for their precision. Bayesian regularization backpropagation training approaches outperformed the remaining three [24] algorithms. Using Django, a second convolutional neural network for classifying skin cancer was developed in [25]. This CNN story classified benign vs, malignant skin tumors. This CNN's dataset comprises of 3297 images of skin cancer retrieved from the Kaggle website. The categorization of skin cancer into two groups and the use of two separate architectural models, each with a different number of parameters, resulted in the establishment of two distinct groups.