Alzheimer’s disease (AD) poses a significant global health challenge, making early and efficient detection essential for timely intervention. While three-dimensional (3D) T1-weighted MRI is widely used for AD detection, its clinical applicability is often hindered by high acquisition costs, prolonged scan durations, complex preprocessing pipelines, and the heavy computational requirements of volumetric deep learning models. Additionally, 3D models typically demand large annotated datasets and extended training time—constraints that limit their deployment in real-world, resource-limited settings. In contrast, two-dimensional (2D) T1-weighted MRI slices provide a cost-effective and computationally efficient alternative. These slices retain localized pathological features vital for diagnosis and can be leveraged effectively through modern representation learning. In practical settings, 3D scans may be missing or degraded, while 2D slices remain accessible and informative. This study introduces a hybrid deep learning-based framework for AD classification using 2D T1-weighted MRI slices from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We introduce a series of methodological innovations, including advanced preprocessing techniques, weighted majority voting with confidence scores, the integration of statistical and pixel-based features, and a hybrid architecture that fuses pre-trained deep learning models with classical machine learning classifiers. Our best-performing model, the Weighted Augmented Hybrid Network (WAHNet), achieves an accuracy of 80.1
In the modern context, as the data generated is exponential, finding meaningful patterns from large datasets is an urgent need. A ‘Topic Evolution Model’ could generate the evolutions related to a topic of user interest and assist in the exploration of patterns. In a generic setting, the proposed ‘topic evolution model’ assists researchers and domain experts for the relevant information extraction on scientific field progress and innovations in technological field or domain from large archives. The evolution patterns uncover the emerging, decay/fading, peculiar, and long-lasting research topics, and subtopics. The performance evaluation on coherence metrics asserts that the proposed model significantly minimizes the domain expert user efforts in topic analysis, as evolving patterns easily reveal underlying statistical and machine learning details. The perplexity metrics highlights the capability of the topic model towards the cognitive view of the user, i.e., change of ideas and knowledge through a period of time reducing the citation bias.
In Magnetic Resonance Imaging (MRI), reduction in scanning time and high reconstruction quality are of paramount value. The recent sparsity based optimization techniques offer a way out to this end. The weighted norm minimization in Compressed Sensing (CS) has become popular due to its capability in providing adaptive sparse solutions. The ℓ _1-p , p>1 , minimization on the other hand has recently gained attention of researchers due to its superiority in dealing with highly coherent matrices. Intending to exploit the best of both, we propose a weighed ℓ _1-p minimization problem for p>1 . While presenting its solver, we demonstrate that the proposed optimization problem reconstructs an MR image faithfully from a small set of its Fourier samples. We compare and contract our method with its existing counterparts that use CS based ideas.
Facial expressions are utilized often in the day to day communication and are considered significant as they can mirror the internal emotional states of a person. Automatic Facial Expression Recognition (FER) systems aim at classifying the facial images into various expressions. To do this task accurately, better feature descriptors are to be developed to effectively capture the facial information. In this regard, novel local texture based feature extraction technique, Petersen Graph based Binary Pattern (PGBP), inspired by the Generalized Petersen Graph has been proposed. PGBP extracts three feature values in a 5 × 5 overlapping neighborhood. The experiments have been performed on MUG, TFEID and KDEF datasets with respect to six expressions in person independent setup. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of recognition accuracy.
Deep Neural Networks (DNN) for resource-constrained mobile devices tend to reduce diagnosis latency and alleviate privacy concerns of the patients in the healthcare domain. Since mobile devices are limited by computational power and battery capacity, traditional DNN requires optimization. This paper analyzes the effect of compression techniques to detect Atrial Fibrillation (AFib) from Electrocardiogram signals. Specifically, we design a framework that compresses Residual Neural Network (ResNet) and Long Short Term Memory (LSTM) model using Weight Pruning, Weight sharing, Quantization, Huffman encoding, and Tensorflow Lite for AFib detection in android devices. The compressed models are evaluated on PhysioNet Computing in Cardiology Challenge 2017 database, and models are evaluated using size, inference time, precision, accuracy, sensitivity, and specificity. The models are tested on desktop computer and android smartphone.
