• Punith G B
• Prasad Reddy G
• Siri Aishwarya G U3
• Sneha A
• Dr Krishna Kumar P R

Skin cancer is one of the most common forms of cancer globally, with melanoma being the deadliest type. Early detection plays a crucial role in improving treatment outcomes and survival rates. Traditional methods of diagnosis, which rely on visual inspection by dermatologists, can be subjective and prone to errors. This paper proposes a novel approach to skin cancer detection by integrating machine learning algorithms with advanced image processing techniques. By analyzing dermatological images of skin lesions, image processing methods such as segmentation, feature extraction, and texture analysis are used to enhance and classify the images for accurate diagnosis. Subsequently, machine learning models, including Convolutional Neural Networks (CNNs), are employed to classify skin lesions as malignant or benign based on extracted features. The system's performance is evaluated using standard datasets, achieving promising results in terms of accuracy, sensitivity, and specificity. This research demonstrates the potential of automated image analysis in aiding early skin cancer detection, offering a reliable, efficient, and scalable solution for clinical applications. The findings highlight the significant role of artificial intelligence in enhancing the diagnostic process and supporting healthcare professionals in making informed decisions

skin cancer,melanoma, image preprocessing, hair removal noise reduction, CNN, ResNet, DenseNet

"Leveraging Machine Learning for Early Detection of Skin Cancer through Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.h507-h512, December-2024, Available :http://www.jetir.org/papers/JETIR2412761.pdf

"Leveraging Machine Learning for Early Detection of Skin Cancer through Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. pph507-h512, December-2024, Available at : http://www.jetir.org/papers/JETIR2412761.pdf