The problem Patronus :-steps towards innovative bharat solves

Our platform for early-stage skin cancer diagnosis integrates cutting-edge technologies and comprehensive patient support services to revolutionize skin health management.

We start by collecting high-resolution images from the HAM10000 dataset and apply preprocessing techniques like Gaussian filtering and image enhancement to optimize image quality. Fractal geometry is then utilized to analyze lesion borders, allowing accurate measurement and identification of irregular shapes indicative of potential malignancy.

To develop our skin cancer detection model, we leverage transfer learning with pre-trained ImageNet models. Fine-tuning these models with the HAM10000 dataset enables our Depthwise Separation Convolutional Neural Networks (CNNs) to effectively classify skin lesions based on learned features.

Integration with the Bhashni API provides multilingual support, ensuring accessibility to diverse populations. Users can receive platform content and interact in their preferred language, enhancing inclusivity, particularly in rural areas.

Beyond diagnosis, our platform offers comprehensive patient support. Users can share analyzed reports with healthcare providers, facilitating timely consultations and treatment planning. Appointment scheduling and record management features enable efficient healthcare management, ensuring continuity of care and personalized treatment.

In summary, our platform combines advanced technology with patient-centric services to enable early detection, personalized care, and improved outcomes in the fight against skin cancer.Our platform for early-stage skin cancer diagnosis integrates cutting-edge technologies and comprehensive patient support services to revolutionize skin health management.

Challenges we ran into

first challenge in racial baisnessed because it's difficult to make the skin tone difference for any ai model,next challenge we faced to enhance our model accuracy after that the major challenge we faced in integration of ml model with web apps and android application it's hard to integrate it