SKIN CANCER PREDICTION

SKIN CANCER PREDICTION

Ms. Gayathri K1, Deepika S2, Harish P3, Kowshik S4

Bachelor of Technology – 3rd Year Department of Artificial Intelligence and Data Science

Sri Shakthi Institute of Engineering and Technology (Autonomous) Coimbatore-641062

ABSTRACT

Melanoma is the most deadly type of skin cancer, and it is one of the most prevalent types in the globe. Increasing survival rates requires precise diagnosis and early identification. Biopsies and dermatologists' eye examinations are two time-consuming and subjective traditional ways of diagnosing skin cancer. Deep learning and machine learning methods have demonstrated significant promise in recent years for helping medical professionals diagnose and detect skin cancer early. In order to classify skin lesions as either benign or malignant, this study investigates the use of computer vision and artificial intelligence (AI) models, particularly convolutional neural networks (CNNs). To train and assess these algorithms, we employ a sizable dataset of photos of skin lesions with annotations. The findings demonstrate the high accuracy, sensitivity, and specificity that AI-based models may attain, making them a promising tool for automated skin cancer detection.

KEYWORDS:
Skin cancer prediction, early detection, machine learning, deep learning, convolutional neural networks, CNNs, dermatology, image classification, healthcare technology, melanoma, basal cell carcinoma, malignant classification, benign classification, medical imaging, predictive analytics, skin lesion analysis, artificial intelligence, AI in healthcare, feature extraction, image preprocessing, skin cancer diagnosis, neural networks, precision medicine, computer-aided diagnosis, digital health, skin cancer screening, supervised learning, health informatics, clinical decision support, cancer prediction model