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.

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

Alzheimer's disease (AD) is the leading neurodegenerative disorder and primary cause of dementia. Researchers are increasingly drawn to automated diagnosis of AD using neuroimaging analyses. Conventional deep learning (DL) models excel in constructing learning classifiers in early-stage AD diagnosis. However, they often struggle with AD diagnosis due to uncertainties stemming from unclear annotations by experts, challenges in data collection, such as data harmonization issues, and limitations in equipment resolution. These factors contribute to imprecise data, hindering accurate analysis, interpretation of obtained results, and understanding of complex symptoms. In response, the integration of fuzzy logic into DL, forming fuzzy deep learning (FDL), effectively manages imprecise data and provides interpretable insights, offering a valuable advancement in AD. Therefore, exploring recent advancements in integrating DL with fuzzy logic is crucial for improving AD diagnosis. In this review, we explore the contributions of fuzzy logic within FDL models, focusing on fuzzy-based image preprocessing, segmentation, and classification. Moreover, in exploring research directions, we discuss the possibility of the fusion of multimodal data with fuzzy logic, addressing challenges in AD diagnosis. Leveraging fuzzy logic and membership while integrating diverse datasets, such as genomics, proteomics, and metabolomics may provide an effective development of a DL classifier. In addition, fuzzy explainable DL promises more accurate and linguistically interpretable decision support systems for AD diagnosis. The primary objective of this article is to serve as a comprehensive and authoritative resource for newcomers, researchers, and clinicians interested in employing FDL models for AD diagnosis.

Alzheimer's disease (AD) is a neurological disorder that primarily affects the elderly and is characterized by cognitive decline and memory loss. Recent research has shown that susceptibility-weighted imaging (SWI) images are useful for diagnosing AD because they reveal abnormally high iron deposition in certain brain regions of people with the disease. Machine learning (ML) algorithms, particularly deep learning (DL) networks, are making incredible strides in AD diagnosis using imaging data to assist physicians in making decisions. The random-vector functional link network (RVFL) is an example of a single-hidden-layer feedforward network that uses a closed-form solution-based approach to offer a variety of feature mapping functions and kernels. In the proposed paper, SWI image features are extracted with a DL network, ResNet 50, and afterward classified with a kernel ridge regression-based RVFL network. To manage data with an unbalanced class distribution, we present a weighted kernel ridge regression-based RVFL network that is capable of generalizing to balanced data. We used SWI images from the publicly accessible OASIS dataset to evaluate the proposed methods for AD diagnosis. Experiment results show that the proposed model outperforms the state-of-the-art models.

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.