Skin Cancer Detection Using Deep Learning
Skin Cancer Detection project leverages advanced deep learning models, including CNN, DenseNet121, and EfficientNetB4, to accurately classify skin cancer images. This initiative aims to improve early diagnosis and patient outcomes.
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Project Outcomes
- Created a mechanical system to classify skin cancer.
- Obtained the accuracy above 80% while using EfficientNetB4.
- We have also employed image augmentation to make the diversity in datasets.
- The DenseNet121 and EfficientNet models were successfully fine-tuned.
- Overall performance was enhanced through the prevention of overfitting by the inclusion of Dropout layers.
- To resize and augmentation of the given image, OpenCV has been used for a faster and more efficient process.
- Skin lesion categories to improve healthcare diagnosis were developed and created.
- Stored trained models and weights so that they can be used later.
- Produced accurate estimations that can assist doctors in skin cancer diagnosis.
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