Skin Cancer Detection

[mynameankita]

Project name

Skin cancer detection

Description

Skin cancer detection involves the meticulous examination of skin lesions for signs indicative of potential malignancy. It begins with visual inspection, during which healthcare professionals scrutinize moles, freckles, or other skin abnormalities for changes in color, size, shape, or texture. Dermatoscopy, a non-invasive technique utilizing specialized equipment, provides a closer examination by revealing underlying structures. Suspicious lesions may undergo biopsy for further evaluation, with tissue samples analyzed by pathologists for cancerous cells. To augment traditional diagnostic methods, computer-aided diagnosis (CAD) systems have emerged, employing machine learning algorithms and image analysis to assist in lesion classification. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown significant promise in automating skin cancer detection through the analysis of large datasets of annotated images. Mobile applications equipped with image analysis capabilities offer accessible tools for users to capture and assess skin lesions, while teledermatology facilitates remote consultation with dermatologists for expert evaluation. Collectively, these approaches aim to enhance the accuracy and efficiency of skin cancer detection, ultimately contributing to improved patient outcomes and prognosis.

Project Repository URL

https://github.com/mynameankita/Skin-Cancer

Project video

---

Team members

https://github.com/mynameankita,https://github.com/harleen192,https://github.com/Chetan-git01001