International Research Journal of Engineering and Technology (IRJET) Volume: 11 Issue: 10 | Oct 2024
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Skin Disease Identification by Images using CNN Dr. P. Durgadevi1, G. Himanshu Dutt2, A.V.V. Lokesh Varma3, Muhammad Rishan4 Asst. Professor1 , Student2 , Student3 , Student4 Department of Computer Science and Engineering1, SRM Institute of Science and Technology, Chennai, India1 ----------------------------------------------------------------------------***-------------------------------------------------------------------------Abstract— The incorporation of machine learning I. INTRODUCTION algorithms has transformed the disease detection approaches on the scene of modern healthcare, especially in situations where symptoms serve as critical diagnostic clues. Adaptable In this work, the CNN algorithm is used as the primary analytical tool, which investigates the field of diagnosing human diseases based on their images symptoms. Because it is adept at processing a wide variety of symptom data and is known for its ensemble learning methodology, it is a great choice for CNN finding complex patterns in challenging data sets. The algorithm captures the subtleties of symptom-disease correlations by combining numerous decisions trees and also ensures resistance and adaptability across a range of drugs conditions.
The study highlights how the system can cope with large dimensions data, enabling the detection of nuances and context-specific symptoms patterns. The results show that CNN significantly improves diagnosis accuracy, which facilitates the identification of early diseases and rapid therapy. The results highlight the accuracy of the algorithm while emphasizing its accuracy the potential to revolutionize healthcare practices by providing doctors data-driven statistics. The implications of this work go far beyond diagnoses; open the door to the day when deep learning algorithms, especially CNN, will be essential for proactive and individualized treatment. Combining computing power with medical knowledge creates opportunities for more tuning the disease, improving patient outcomes, streamlining treatments and a shift in the focus of medicine towards personalization and preventive therapy. Keywords — CNN, HAM10000 dataset, Skin disease diagnosis Image-based classification, Dermatology, Melanocytic nevi (NV) Melanoma (MEL), Basal Cell Carcinoma (BCC), Actinic Keratoses (AKIEC)
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Advances in machine learning, particularly convolutional neural networks (CNNs), have revolutionized the field of medical diagnostics, including the identification of skin diseases. Unlike traditional diagnostic techniques, which rely heavily on human expertise and can be prone to error due to variability in judgement, machine learning algorithms offer a more consistent and accurate alternative. By analyzing large datasets of medical images, CNNs can detect subtle patterns, textures, and features in skin lesions that the human eye might miss. These models can distinguish between different skin conditions such as melanocytic nevi, dermatofibroma, melanoma, vascular lesions, and basal cell carcinoma with remarkable accuracy. CNNs excel in extracting high-level features from raw image data, automating the diagnostic process and increasing its reliability. The use of CNNs in healthcare has the potential to significantly improve patient outcomes. Early detection of skin diseases, especially malignant forms such as melanoma, can lead to early interventions, reduced mortality and improved prognosis. Additionally, CNN-based systems can be deployed in remote or underserved areas where access to dermatology expertise is limited, providing scalable solutions to global health problems. In addition, CNNs can be integrated into mobile applications and wearable devices, enabling real-time monitoring of skin conditions. This democratizes healthcare by empowering patients to take an active role in managing their health. These systems can also assist dermatologists by serving as a second opinion and increasing the overall accuracy of diagnoses and treatment plans. In summary, the application of CNN in the diagnosis of skin diseases is a transformative development in medical technology. It reduces the likelihood of human error, provides faster and more accurate results, and offers the potential for early detection, ultimately contributing to better patient care. As research in this field continues to expand, CNN-based diagnostics is poised to play a key role in shaping the future of healthcare.
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