SKIN CANCER DETECTION USING CNN

SKIN CANCER DETECTION USING CNN

Amulya K 1, Prof. Sowmya M S 2

1 Student, Department of MCA, Bangalore Institute of Technology, Karnataka, India

2 Professor, Department of MCA, Bangalore Institute of Technology, Karnataka, India

ABSTRACT:

Skin cancer is the most common cancer in the world, but it is also one for which early detection makes the difference in effective treatment and patient outcomes. We rely on a fine-tuned, fine-classified, state-of-the-art deep learning model pre-trained on innumerous skin images from a convolutional neural network. This already gives us very high-performance classification, which surpasses, in turn, all previous methods at identifying malignant and benign lesions. This AI-based approach ensures reliable and accurate classification, which is important for earlier diagnosis and treatment.

It possesses a highly effective, easily adaptable, and user-friendly web interface that permits uploading skin image input for diagnosis then makes available the corresponding real-time output to the diagnostician. This does not only offer an easier process for patients to seek preliminary examinations but also offers support to dermatologists and healthcare practitioners. The tool can therefore be introduced in the workflow of medical practitioners so that it can make much more informed decisions related to diagnosis and treatment of lesions, which helps in improving patient outcomes. This should dramatically improve early detection and lead to better pathways of care for skin cancer patients from around the world.

Keywords: AI- Artificial Intelligence, DL- Deep learning, CNN- Convolutional Neural Network, HAM- Human Against Machine