Tallha Akram

TL;DR: The proposed technique incorporates two major steps of infected regions detection and finally feature extraction and classification, and outperforms several existing methods in terms of greater precision and improved classification accuracy.

TL;DR: A fully automated computerized aided diagnosis (CAD) system is proposed based on the deep learning framework and a fair comparison with other state-of-the-art is provided to further increase confidence in the proposed framework.

TL;DR: An automated system is developed for tumor extraction and classification from MRI based on marker‐based watershed segmentation and features selection that outperforms existing methods with greater precision and accuracy.

TL;DR: This research proposes a hybrid strategy for efficient classification of human activities from a given video sequence by integrating four major steps: segment the moving objects by fusing novel uniform segmentation and expectation maximization, extract a new set of fused features using local binary patterns with histogram oriented gradient and Harlick features, and feature classification using multi-class support vector machine.

TL;DR: Simulation results reveal that the proposed method performs exceptionally better compared with existing works, and different performance measures are considered.