What is it about?
It takes the image as input, and the name of the disease and control measures are displayed as output. The application takes the input image, processes it with the dataset already given, and categorises the disease. The name of the disease and tips to reduce the spread of the disease are given to the user. Python code is used to process images, and we employ a method called deep convolution neural networks (CNN). We are categorising the given image into four types: acne, benign, malignant, and skin allergy. The datasets are collected from different sources, such as Kaggle, GitHub, and Google. There are approximately 3003 training datasets and 600 test datasets in total. This network will give an accuracy of 98.7% and produce results quicker than the formal technique, making this submission an effective and reliable system for dermatologic disease detection.
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Why is it important?
Skin diseases differ largely in symptoms and severity. There are almost 3,003 types of skin diseases known as dermatology [1]. The most common diseases are vascular lesions, actinic keratosis, eczema, shingles, hives, sunburn, dermatitis, diaper rash, and basal cell carcinoma [2]. They can be temporary or permanent and may be painless or severe. A few may occur as a result of our environment and climate, or they may be genetic. A few of them might be minor, while a few might be hazardous to life [3]. But, luckily, due to the development in technology in the arena of lasers and medical technology [4], we have been able to detect the disease at an earlier stage and prompt it accurately.
Perspectives
This perspective would explore how CNNs are employed to analyze dermatological images, including the architecture of these networks, their training processes, and their ability to recognize patterns in skin lesions. t would assess the performance of CNNs in terms of accuracy, sensitivity, and specificity in detecting various dermatological conditions compared to traditional methods. The review would focus on how CNNs contribute to the early detection and diagnosis of dermatological diseases, such as melanoma, psoriasis, eczema, and other skin conditions.
Vaseem Akram Shaik
Read the Original
This page is a summary of: Dermatological disease detection and preventative measures using deep convolution neural networks, January 2024, American Institute of Physics,
DOI: 10.1063/5.0195880.
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