Alzheimer’s disease (AD) is a neurological condition primarily impacting the elderly and is known for its progressive decline in normal brain functioning. The magnetic resonance imaging (MRI) modality enables disease diagnoses by identifying atrophy patterns and structural changes. Imaging captures anatomical details effectively using axial, sagittal, and coronal planes. The axial plane of MRI provides a cross-sectional view, whereas the coronal plane allows visualization in the anterior-posterior direction. The sagittal plane offers a lateral view and aids in examining asymmetry and bilateral structures of the brain’s anatomy. In this paper, the features of the sagittal plane are extracted using Resnet-50, a deep-learning network. These extracted features are fed to the classifier for AD diagnosis. This paper presents a l1 regularization-based random vector functional link network (RVFL) classifier for AD diagnosis. The optimization problem of the proposed l1 regularization-based RVFL is solved using the Split Bregman iterative method. l1 regularization-based RVFL classifier is more generalizable and produces sparse output. The sparse output indicates a lower number of non-zero elements in the output compared to the standard RVFL network which uses l2 regularization. The experiments are performed on the publicly accessible Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, compared with other feed-forward networks. Results highlight the better performance of the proposed model for AD diagnosis than state-of-the-art approaches.

As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.

Diabetic retinopathy (DR) is a complication of diabetic Mellitus, developing retinal lesions that impair vision. The DR detection in the early stages avoids permanent vision loss. The treatments provide relief, and the vision loss due to DR is irreversible. The manual grading of DR is time-consuming and prone to human errors. The other real-time problem is exchanging patient fundus image information with hospitals worldwide while upholding the organisations’ privacy concerns. When training a deep learning (DL) network, two critical factors to keep in mind are creating a collaborative platform and protecting patient data privacy. Therefore, an automated DR detection technique is required while protecting patient data and privacy. In this work, we propose a novel DR severity grading technique based on Federated Learning (FL), a recent advancement in DL called DRFL. FL is a new research paradigm that allows DL models to be trained collectively without disclosing clinical information. In DRFL, we combined the Federated averaging (FedAvg) technique and the median of the categorical cross-entropy loss. Since in comparison to FedAvg, the median cross-entropy is better suited for either under-fitted or over-fitted clients. Also, we propose a novel central server that extracts multi-scale features from the fundus images to identify small lesions present in the fundus image. In this work, we consider five clients holding different preprocessed fundus images collected from publicly available databases such as MESSIDOR-2, IDRiD, Kaggle, and a local database collected from Silchar Medical College and Hospital. The proposed model obtained an accuracy of 98.6%, specificity of 99.3%, precision of 97.25%, and an F1 score of 97.5%, which are better results than other techniques.

Summary The image dehazing stage is used significantly as a preprocessing step for various applications such as remote sensing and long range imaging and automatic driver assistance system. Images acquired under low illumination, fog and snow conditions frequently show qualities like low contrast and low brightness, which genuinely influence the enhanced abstract visualization on natural eyes and extraordinarily limit the exhibition of different machine vision frameworks. The images that are captured in low‐light or heavy fog might have salient features that cannot be extracted using standard computer vision systems. A good way to get the enhanced image is to determine the transmission map (haze density or low illumination parameters) of air‐light media from each pixel of the input image. In this article, an improved U‐Net architecture is proposed to enhance images and provide robust performance metrics against the existing methods. In this model, the pooling operations in generalized U‐Net architecture are replaced by discrete wavelet transform based on up and down samplings. An attention module is developed by fusing both up and down samples to identify the missing information of low‐level features in up‐samples. The proposed architecture for U‐Net tested with different datasets: See‐in‐the‐Dark (SID) dataset, Exclusively Dark Image Dataset (ExDark), Realistic Single Image Dehazing (RESIDE) dataset, and few real‐time images and achieves superior performance metrics in terms of PSNR, MSE, and SSI when compared to the other state‐of‐art methods.

Summary In recent times the late fusion of features approach for high‐level features extracted by multiple deep convolutional neural networks (DCNNs) has proven to be very effective in the computer vision field, especially for object classification problems. Pretrained DCNNs DenseNet‐121 and ResNet‐18 are retrained, keeping the number of output nodes equal to the number of classes present in the dataset. The last fully connected layers of these networks thereby get adapted to transform the high‐level features to a low‐dimensional feature map. Then these maps are fused to improve the performance of the model. On the other hand, an ensemble of multiple classifiers reduces the overfitting problem by combining multiple models prediction matrices. In this work, the prediction matrices of two Logsoftmax multiclass classifiers are combined. The feature maps for these two classifiers are extracted using pretrained DenseNet and ResNet. This study compares the late fusion of high‐level features approach and ensemble of multiple classifiers approach for object classification problems. Experimentation has been carried out on two benchmark datasets, such as CIFAR‐10 and CIFAR‐100, and it achieves 96.48% and 83.33% of test accuracy for ensemble of multiple classifiers and the late feature fusion approach. The proposed method has been compared with other deep architectures and datasets.