Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However, Deep CNNs require substantial computational power and memory, particularly for large datasets and complex architectures. Additionally, optimising the hyperparameters of deep CNNs, although critical for enhancing model performance, is challenging due to the high computational costs involved, making it difficult without access to high-performance computing resources. To address these limitations, this study presents a fast and efficient model that aims to achieve superior classification performance compared to popular Deep CNNs by developing lightweight CNNs combined with the Nonlinear Lévy chaotic moth flame optimiser (NLCMFO) for automatic hyperparameter optimisation. NLCMFO integrates the Lévy flight, chaotic parameters, and nonlinear control mechanisms to enhance the exploration capabilities of the Moth Flame Optimiser during the search phase while also leveraging the Lévy flight theorem to improve the exploitation phase. To assess the efficiency of the proposed model, empirical analyses were performed using a dataset of 2314 brain tumour detection images (1245 images of brain tumours and 1069 normal brain images). The evaluation results indicate that the CNN_NLCMFO outperformed a non-optimised CNN by 5% (92.40% accuracy) and surpassed established models such as DarkNet19 (96.41%), EfficientNetB0 (96.32%), Xception (96.41%), ResNet101 (92.15%), and InceptionResNetV2 (95.63%) by margins ranging from 1 to 5.25%. The findings demonstrate that the lightweight CNN combined with NLCMFO provides a computationally efficient yet highly accurate solution for medical image classification, addressing the challenges associated with traditional deep CNNs.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, both in terms of prevention and treatment, it is very beneficial to patients. Therefore, our team proposed a novel deep learning model named 3D-CNN-VSwinFormer. The model consists of two components: the first part is a 3D CNN equipped with a 3D Convolutional Block Attention Module (3D CBAM) module, and the second part involves a fine-tuned Video Swin Transformer. Our investigation extracts features from subject-level 3D Magnetic resonance imaging (MRI) data, retaining only a single 3D MRI image per participant. This method circumvents data leakage and addresses the issue of 2D slices failing to capture global spatial information. We utilized the ADNI dataset to validate our proposed model. In differentiating between AD patients and cognitively normal (CN) individuals, we achieved accuracy and AUC values of 92.92% and 0.9660, respectively. Compared to other studies on AD and CN recognition, our model yielded superior results, enhancing the efficiency of AD diagnosis.

Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and patient care. Deep convolutional neural networks trained on MRI data can enhance diagnostic precision. This study aims to utilize deep convolutional neural networks (CNNs) trained on MRI data for Alzheimer's disease diagnosis and classification. In this study, the Alzheimer MRI Preprocessed Dataset was used, which includes 6735 brain structural MRI scan images. After data preprocessing and normalization, four models Xception, VGG19, VGG16 and InceptionResNetV2 were utilized. Generalization and hyperparameter tuning were applied to improve training. Early stopping and dynamic learning rate were used to prevent overfitting. Model performance was evaluated based on accuracy, F-score, recall, and precision. The InceptionResnetV2 model showed superior performance in predicting Alzheimer's patients with an accuracy, F-score, recall, and precision of 0.99. Then, the Xception model excelled in precision, recall, and F-score, with values of 0.97 and an accuracy of 96.89. Notably, InceptionResnetV2 and VGG19 demonstrated faster learning, reaching convergence sooner and requiring fewer training iterations than other models. The InceptionResNetV2 model achieved the highest performance, with precision, recall, and F-score of 100% for both mild and moderate dementia classes. The Xception model also performed well, attaining 100% for the moderate dementia class and 99-100% for the mild dementia class. Additionally, the VGG16 and VGG19 models showed strong results, with VGG16 reaching 100% precision, recall, and F-score for the moderate dementia class. Deep convolutional neural networks enhance Alzheimer's diagnosis, surpassing traditional methods with improved precision and efficiency. Models like InceptionResnetV2 show outstanding performance, potentially speeding up patient interventions.

Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases cause minor changes in the brain's anatomy. Magnetic Resonance Imaging (MRI) is a vital tool for diagnosing and visualizing these ND; however, standard techniques contingent upon human analysis can be inaccurate, require a long-time, and detect early-stage symptoms necessary for effective treatment. Spatial Feature Extraction (FE) has been improved by Convolutional Neural Networks (CNN) and hybrid models, both of which are changes in Deep Learning (DL). However, these analysis methods frequently fail to accept temporal dynamics, which is significant for a complete test. The present investigation introduces the STGCN-ViT, a hybrid model that integrates CNN + Spatial-Temporal Graph Convolutional Networks (STGCN) + Vision Transformer (ViT) components to address these gaps. The model causes the reference to EfficientNet-B0 for FE in space, STGCN for FE in time, and ViT for FE using AM. By applying the Open Access Series of Imaging Studies (OASIS) and Harvard Medical School (HMS) benchmark datasets, the recommended approach proved effective in the investigations, with Group A attaining an accuracy of 93.56%, a precision of 94.41% and an Area under the Receiver Operating Characteristic Curve (AUC-ROC) score of 94.63%. Compared with standard and transformer-based models, the model attains better results for Group B, with an accuracy of 94.52%, precision of 95.03%, and AUC-ROC score of 95.24%. Those results support the model's use in real-time medical applications by providing proof of the probability of accurate but early-stage ND diagnosis.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosis remains inadequate, with projections estimating over 55 million affected individuals by 2022, expected to triple by 2050. Accurate early detection is critical for effective intervention. This study presents Neuroimaging-based Early Detection of Alzheimer's Disease using Deep Learning (NEDA-DL), a novel computer-aided diagnostic (CAD) framework leveraging a hybrid ResNet-50 and AlexNet architecture optimized with CUDA-based parallel processing. The proposed deep learning model processes MRI and PET neuroimaging data, utilizing depthwise separable convolutions to enhance computational efficiency. Performance evaluation using key metrics including accuracy, sensitivity, specificity, and F1-score demonstrates state-of-the-art classification performance, with the Softmax classifier achieving 99.87% accuracy. Comparative analyses further validate the superiority of NEDA-DL over existing methods. By integrating structural and functional neuroimaging insights, this approach enhances diagnostic precision and supports clinical decision-making in Alzheimer's disease detection.

We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) detection that constructs complex ML predictors from modular components. In our directed computational graph, datasets are represented as nodes [Formula: see text], and deep learning (DL) models are represented as directed edges [Formula: see text], allowing us to model complex image-processing pipelines [Formula: see text] as end-to-end DL predictors. Each directed path in the graph functions as a DL predictor, supporting both forward propagation for transforming data representations, as well as backpropagation for model finetuning, saliency map computation, and input data optimization. We demonstrate our model on Alzheimer's disease prediction, a complex problem that requires integrating multimodal data containing scans of different modalities and contrasts, genetic data and cognitive tests. We built a graph of 11 nodes (data) and 14 edges (ML models), where each model has been trained on handling a specific task (e.g. skull-stripping MRI scans, AD detection,image2image translation, ...). By using a modular and adaptive approach, our framework effectively integrates diverse data types, handles distribution shifts, and scales to arbitrary complexity, offering a practical tool that remains accurate even when modalities are missing for advancing Alzheimer's disease diagnosis and potentially other complex medical prediction tasks.

Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pediatric gliomas from the BraTS-PEDs 2024 dataset. First, a pre-trained SegResNet model was retrained with a transfer learning approach and tested on the pediatric cohort. Then, two novel multi-encoder architectures leveraging the attention mechanism were designed and trained from scratch. To enhance the performance on ET regions, an ensemble paradigm and post-processing techniques were implemented. Overall, the 3-encoder model achieved the best performance in terms of Dice Score on TC and WT when trained with Dice Loss and on ET when trained with Generalized Dice Focal Loss. SegResNet showed higher recall on TC and WT, and higher precision on ET. After post-processing, we reached Dice Scores of 0.843, 0.869, 0.757 with the pre-trained model and 0.852, 0.876, 0.764 with the ensemble model for TC, WT and ET, respectively. Both strategies yielded state-of-the-art performances, although the ensemble demonstrated significantly superior results. Segmentation of the ET region was improved after post-processing, which increased test metrics while maintaining the integrity of the data.

In recent years, machine learning and deep learning have shown potential for improving Parkinson's disease (PD) diagnosis, one of the most common neurodegenerative diseases. This comprehensive analysis examines machine learning and deep learning-based Parkinson's disease diagnosis using MRI, speech, and handwriting datasets. To thoroughly analyze PD, this study collected data from scientific literature, experimental investigations, publicly accessible datasets, and global health reports. This study examines the worldwide historical setting of Parkinson's disease, focusing on its increasing prevalence and inequities in treatment access across various regions. A comprehensive summary consolidates essential findings from clinical investigations and pertinent datasets related to Parkinson's disease management. The worldwide context, prospective treatments, therapies, and drugs for Parkinson's disease have been thoroughly examined. This analysis identifies significant research deficiencies and suggests future methods, emphasizing the necessity for more extensive and diverse datasets and improved model accessibility. The current study proposes the Meta-Park model for diagnosing Parkinson's disease, achieving training, testing, and validation accuracy of 97.67 %, 95 %, and 94.04 %. This method provides a dependable and scalable way to improve clinical decision-making in managing Parkinson's disease. This research seeks to provide innovative, data-driven decisions for early diagnosis and effective treatment by merging the proposed method with a thorough examination of existing interventions, providing renewed hope to patients and the medical community.

Despite the widespread use of stereotactic radiosurgery (SRS) to treat cerebral arteriovenous malformations (AVMs), this procedure can lead to radiation-induced changes (RICs) in the surrounding brain tissue. Volumetric assessment of RICs is crucial for therapy planning and monitoring. RICs that appear as hyper-dense areas in magnetic resonance T2-weighted (T2w) images are clearly identifiable; however, physicians lack tools for the segmentation and quantification of these areas. This paper presents an algorithm to calculate the volume of RICs in patients with AVMs following SRS. The algorithm could be used to predict the course of RICs and facilitate clinical management. We trained a Mask Region-based Convolutional Neural Network (Mask R-CNN) as an alternative to manual pre-processing in designating regions of interest. We also applied transfer learning to the DeepMedic deep learning model to facilitate the automatic segmentation and quantification of AVM edema regions in T2w images. The resulting quantitative findings were used to explore the effects of SRS treatment among 28 patients with unruptured AVMs based on 139 regularly tracked T2w scans. The actual range of RICs in the T2w images was labeled manually by a clinical radiologist to serve as the gold standard in supervised learning. The trained model was tasked with segmenting the test set for comparison with the manual segmentation results. The average Dice similarity coefficient in these comparisons was 71.8%. The proposed segmentation algorithm achieved results on par with conventional manual calculations in determining the volume of RICs, which were shown to peak at the end of the first year after SRS and then gradually decrease. These findings have the potential to enhance clinical decision-making. Not applicable.

Functional magnetic resonance imaging (fMRI) has opened new frontiers in neuroscience by instrumentally driving our understanding of brain function and development. Despite its substantial successes, fMRI studies persistently encounter obstacles stemming from inherent, unavoidable physiological confounds. The adverse effects of these confounds are especially noticeable with higher magnetic fields, which have been gaining momentum in fMRI experiments. This review focuses on the four major physiological confounds impacting fMRI studies: low-frequency fluctuations in both breathing depth and rate, low-frequency fluctuations in the heart rate, thoracic movements, and cardiac pulsatility. Over the past three decades, numerous correction techniques have emerged to address these challenges. Correction methods have effectively enhanced the detection of task-activated voxels and minimized the occurrence of false positives and false negatives in functional connectivity studies. While confound correction methods have merit, they also have certain limitations. For instance, model-based approaches require externally recorded physiological data that is often unavailable in fMRI studies. Methods reliant on independent component analysis, on the other hand, need prior knowledge about the number of components. Machine learning techniques, although showing potential, are still in the early stages of development and require additional validation. This article reviews the mechanics of physiological confound correction methods, scrutinizes their performance and limitations, and discusses their impact on fMRI studies.