Closed SayantikaLaskar closed 1 week ago
Our team will soon review your PR. Thanks @SayantikaLaskar :)
@abhisheks008 There were issues while cloning the repo. I tried it several times but couldn't solve it. The files where changes has been made had invalid path address as stated while cloning. I dont know what to do. Please instruct me what can I do. Thank you.
Re-do the whole thing may be your forked repository is to validated with the main branch.
I have created a new pull request. Please look into it @abhisheks008 Thank you
Pull Request for DL-Simplified 💡
Issue #771
Issue Title : Kidney Stone Detection
GSSoC 2024 participant
Closes: #issue number that will be closed through this PR
Describe the add-ons or changes you've made 📃
This project aims to develop and compare the performance of three advanced deep learning architectures—VGG-like, CNN with spatial attention, and ResNet-like—for classifying images into four categories: Normal, Cyst, Tumor, and Stone. The dataset, consisting of about 12,000 images, is split into training, validation, and test sets. Each model is trained with augmented data to improve generalization. Using EarlyStopping, ModelCheckpoint, and ReduceLROnPlateau callbacks, the models are optimized and their performances are evaluated based on metrics like accuracy, precision, recall, and F1-score. The goal is to determine which architecture best balances accuracy and generalization, offering insights into the benefits of spatial attention and residual connections in deep learning for medical imaging.
Type of change ☑️
What sort of change have you made:
How Has This Been Tested? ⚙️
Describe how it has been tested The project involved evaluating three deep learning architectures—VGG-like, CNN with spatial attention, and ResNet-like—for classifying kidney condition images into four classes (Normal, Cyst, Tumor, and Stone). The dataset, consisting of 12,000 images, was split into training, validation, and test sets. Training data underwent extensive augmentation to enhance model generalization. Each model was trained with categorical cross-entropy loss and Adam optimizer, using callbacks for early stopping, best model checkpointing, and learning rate reduction. Performance was assessed on a test set using accuracy, precision, recall, F1-score, and confusion matrices, with results visualized through accuracy curves and detailed classification reports. This comprehensive evaluation aimed to identify the most effective architecture for accurate kidney condition classification.
Checklist: ☑️