Chest X-ray - Tuberculosis Disease Prediction

[aaradhyasinghgaur]

Is your feature request related to a problem? Please describe. The project is valuable for research in neurology, radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of the condiitons for diseases related to chest X-rays.

Describe the solution you'd like

1. Data Preparation: Collect and preprocess a balanced dataset of real and fake face images, including normalization, resizing, and augmentation.
   2. Base Model Selection: EfficientNetB0,VGG16 , Xception , InceptionV3 like 5 different models excluding its top layers, to leverage its learned features.
   3. Model Construction: Add custom layers on top of the base model for binary classification, compiling with appropriate loss and metrics.
   4. Initial Training: Train the model with the base layers frozen to only update the new layers.
   5. Fine-Tuning: Unfreeze some or all of the base model layers and continue training with a lower learning rate to fine-tune the entire network. 6.) EDA analysis. 7.) Comaprioson using performance matrices such as accuracy scores , confusion matrix etc.

Dataset I'll use :- https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context Add any other context or screenshots about the feature request here.

[aaradhyasinghgaur]

@TAHIR0110 kindly assign this issue to me.

[TAHIR0110]

@kyra-09 assigned as level3!