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@article{206750,
        author = {Varshini and Sudarshan K and Swathi and Harshini P K and Sanvi S Shetty},
        title = {Skin Cancer Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {302-308},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206750},
        abstract = {With an always rising number of cases and a fast progression, skin cancer, especially melanoma, has transformed into a very critical public health issue. To detect it as soon as possible, this automated system based on a Deep Learning model: MobileNetV2 is created. The small size and high accuracy of this model MobileNetV2 was the key reason for considering it. During the training of the model, the dataset used was over 10,000 images. To increase the reliability, all the images went through several preprocessing steps, which includes resizing and a method called “data augmentation” for training the model to recognize melanoma in different situations. The system has achieved an accuracy of about 90% for detecting melanoma. In order to create a user interface that is easy to use without reading a manual first, Gradio was chosen and implemented an upload button that lets the users to upload the images directly from their hard drive. After uploading a photo, the user immediately receives a result and a confidence score. The aim is to support the doctors in filtering out potential melanomas to avoid them from getting lost in a backlog and to ensure that patients with severe skin cancer do not have to wait for too long.},
        keywords = {Deep Learning, Gradio, Melanoma Detection, MobileNet},
        month = {July},
        }