Publications
Iftekhar Ahmed, et al. (2024). Heart Disease Prediction through Enhanced Machine Learning and Diverse Feature Selection Approaches. In IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), pp. 119-124. IEEE. July 30-31, 2024, Bandung, Indonesia. DOI:10.1109/ICSIMA62563.2024.10675564
Iftekhar Ahmed, et al. (2022). Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction. In IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0417-0423. IEEE. October 12-15, 2022, Vancouver, BC, Canada. DOI:10.1109/IEMCON56893.2022.9946591
Ongoing Research
Multi-Model Attentional Fusion Ensemble: A Novel Approach for Accurate Skin Cancer Classification
- Developed an ensemble model for skin cancer classification using ResNet50V2, MobileNetV2, and EfficientNetV2, enhanced by an attention mechanism for improved accuracy.
- Tackled class imbalance with resampling, augmentation, and hair removal, improving the HAM10000 dataset quality.
- The extended Abstract of this work is presented at the IEEE CS BDC Summer Symposium.
A Novel Ensemble Based Deep Learning Model for Accurate Kidney Disease Diagnosis
- Developed an ensemble deep-learning model with 96% accuracy on 12,446 CT kidney images.
- Evaluated transfer learning models, including EfficientNetV2, InceptionNetV2, MobileNetV2, and Vision Transformer (ViT), with ViT achieving 91.5% accuracy.
- Analyzed performance using precision, recall, F1-score, and AUC, showing strong diagnostic capability.
Segmentation on Panoramic Dental X-Ray Images Using U-Net Architectures
- Conducting a comparative performance analysis of various U-Net architecture variants (Vanilla U-Net, Dense U-Net, Attention U-Net, SE U-Net, Residual U-Net, and R2 U-Net) for dental X-ray image segmentation.
- Optimizing convolutional layer configurations to enhance segmentation accuracy and computational efficiency in U-Net models.
Segmentation-Enhanced Deep Learning Utilizing SAM for Freshwater Fish Species Classification in Resource-Constrained Regions
- Developing a deep learning approach for freshwater fish classification in Bangladesh.
- Using the "segment-anything-model" to create precise mask images for U-Net fish segmentation.
- Focused on localized features to boost accuracy and prevent deceptive practices.
- Analyzing 2300 images, with preliminary results showing improved accuracy.