Diagnosis of Skin Diseases and Cancers from Dermoscopicimages with Feature Extraction Impact Approach from Convolutional Neural Network and Ensemble Classification in Increasing the Speed and Accuracy of Diagnosis

The modern world today allows images to be received and stored digitally. To get better results, it is sometimes necessary to make changes to these images. These changes pursue three main goals: image processing, analysis, and comprehension. For this reason, computer image processing systems have been developed to perform these operations with better speed and accuracy. Four major processes occur in these systems: preprocessing, image quality enhancement, image conversion, and image classification and analysis. In these methods, using mathematics, rules have been created by the computer to simulate human visual elements, and it is an aspect of image analysis that is used for specific purposes. Skin imaging systems provide the ability to process images in high volume and with minimal time and cost, as well as increase the accuracy of diagnosis and classification of diseases. These systems, fatigue, human error and other weaknesses that the diagnostician can suffer. Do not have it (1). The first step in diagnosing skin diseases and analyzing digital images of patients with skin lesions is to take a color photograph of the lesion area. One of the most valid methods for this is the use of a dermoscopic device (2). Dermoscopy, also known as dermatoscopy, is an effective tool for dermatologists involved in early diagnosis. Using dermoscopically evaluated pigmented lesions, abnormal structural features are detected and the border of the lesions is accurately observed (3). Accordingly, benign lesions can be detected without the need for biopsy. Dermoscopy increases the accuracy of the diagnosis and helps GPs to correctly identify people with suspected lesions who need to be referred to a specialist. Dermoscopy is also effective in diagnosing non-pigmented skin lesions and inflammatory dermatoses. In dermoscopy, the skin is examined using a special microscope (4).

The proposed algorithm of this research can be divided into 7 separate steps (loading data set, data integration: data balancing with data amplification or data augmentation technique, data cleaning: clearing images to remove hair noise, slicing images to separate skin from skin Healthy, data conversion: data preparation, convolution neural network design (CNN) and training of the proposed model for image feature extraction, classification combination and mass learning by majority voting method). Which was implemented in Python language in Google Columbine environment and supervised. For this study, 25,331 dermoscopic images consisting of skin lesions were included (70% educational images, 15% experimental and 15% validation). In data preprocessing, the data were balanced, then the data cleaning operation was performed to remove hair noise, and the data reduction operation was performed to segment the images by separating the lesion from healthy skin. In the next process By designing the convolution neural network, training data were extracted for feature extraction, and by combining the classifiers, an automated system for diagnosing skin diseases was created and evaluated in dermoscopic images.

In the proposed method of hair noise removal, the quality of images is increased and also the separation of the lesion from healthy skin is optimally designed to accelerate image processing to extract high-level features in the convolutional neural network and increase the accuracy of diagnosis and classification to create An automated diagnostic system is a feature of this study compared to other studies. According to the research results, the use of an automated system for the diagnosis and classification of skin diseases and cancers for health-related care is recommended.

Today, the applications of artificial intelligence are not hidden from anyone. Among these, machine learning as one of the most important branches of this field has a special place in all sciences. Deep learning has proven its worth by using the basics of artificial neural networks in solving many issues in the field of medical image processing such as classification. Experts also based on various experiences of using training methods to conclude that there is no single specific training algorithm that can be successful for all applications and has the highest accuracy. Hence they suggest combined learning. According to the important results, although each of the algorithms had a successful performance individually, but combining several algorithms with each other has led to higher accuracy and less error-making decisions. This study is a step towards helping physicians and specialists in diagnosing skin diseases and benign and malignant skin cancers and can help GPs or other physicians to better manage high-risk lesions. Secondary triage as well as avoid unnecessary treatments and minimize biopsy, which is an invasive and costly procedure. This research helps to provide health-related care, forecasting and treatment, as well as cost savings for both patients and health care providers. Also, in deprived areas and far from the specialist, dermoscopic devices with the help of this algorithm can cause timely treatment and reduce patients' costs and time in the field of diagnosis and, if necessary, referral of patients to the desired specialist. Available as a commercial software package. This software package has the ability to connect to dermoscopic devices. By connecting this software package to dermoscopy, a device is created to quickly diagnose skin diseases and cancers. The greatest value of this dissertation is that it is used as a benchmark for designing future studies and evaluating skin cancer diagnosis techniques in patients who are usually examined by a general practitioner and specialist. The findings of the present study are also consistent with the results of Andre and Pachko (2019) research on the diagnosis of skin cancer based on deep learning and entropy for Perth samples (6).