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Skinno - Can we detect Skin Cancer?
We propose the building of a system that is able to accurately detect skin cancer in a patient using Image Processing and AI Techniques. The user, provided with an android/Web interface, has to upload the required image. Immediately results will be available for further actions. ...learn more
Project status: Published/In Market
Artificial Intelligence, Cloud
Intel Technologies
Intel NUC,
Intel Python
Overview / Usage
In addition to the skin cancer detection, we believe that Emotional Endurance during the healing phase is extremely important, as a result, we have a provision for: * Access to talks by Expert Doctors. * Previous Experiences and Blogs. * Provision to book appointments with doctors. * Chatbots for emotional support.(Future Work)
Methodology / Approach
Proposed Solution: We propose the building of a system that is able to accurately detect skin cancer in a patient using Image Processing and AI Techniques. The user, provided with an android/Web interface, has to upload the required image. The model which is on our web server takes in the input of images of suspected locations for skin cancer either from a web application or an android device and gives whether the cancer is malignant or benign. This helps the doctors saving their time whilst having accuracy on par with humans. The entire system is user-friendly, real-time, and very precise. We have used a transfer learning-based deep neural network, VGG-16 for training the model to predict the probability of the presence of skin cancer.**
Salient Features: Highly Robust, can process all inputs with relative ease. The entire ecosystem is Plug-and-Play, hence the user experience and ease of adoption are quite high. Highly accurate Diagnosis .(92%) Scalable as the entire system runs on the cloud, and results are obtained in a quick manner.
Conclusion: We believe that our solution will help doctors in identifying skin cancer patients and also provide them with appropriate medical and emotional support throughout their recovery journey.
Technologies Used
AI, Machine Learning, Deep Learning.
Collaborators