The project uses a Convolutional Neural Network based model to assess the skin lesions and determine whether the person is affected with Melanoma or other diseases.
To build a CNN based model which can accurately detect melanoma. Melanoma is a type of cancer that can be deadly if not detected early. It accounts for 75% of skin cancer deaths. A solution that can evaluate images and alert dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.
- Actinic keratosis
- Basal cell carcinoma
- Dermatofibroma
- Melanoma
- Nevus
- Pigmented benign keratosis
- Seborrheic keratosis
- Squamous cell carcinoma
- Vascular lesion
- Loading of Data
- Data Visualisation
- Creation of base model
- Choosing an Augmentation strategy and building the model
- Training and evaluating the augmented model
- Dealing with Class Imbalance using Augmentator
- BUilding the final model and Evaluating it.
The final model achieved an accuracy of 91% with a loss of 0.25. The Validation accuracy we achieved with model is 86%. The model is a good fit. Using the Augmentor and dealing with class imbalance helped the model to achieve better accuracy.