Alzheimer’s Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic and detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods and Machine Learning Methods that utilize Biomarker Methods, Fusion, and Registration for multimodality, to pre-process medical scans. The use of automated pipelines and machine learning systems has proven beneficial in accurately identifying AD and its stages, with a success rate of over 95% for single and binary class classifications. However, there are still challenges in multi-class classification, such as distinguishing between AD and MCI, as well as sub-stages of MCI. The research also emphasizes the significance of using multi-modality approaches for effective validation in detecting AD and its stages.

Alzheimer’s disease (AD) is an old-age disease that comes in different stages and directly affects the different regions of the brain. The research into the detection of AD and its stages has new advancements in terms of single-modality and multimodality approaches. However, sustainable techniques for the detection of AD and its stages still require a greater extent of research. In this study, a multimodal image-fusion method is initially proposed for the fusion of two different modalities, i.e., PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging). Further, the features obtained from fused and non-fused biomarkers are passed to the ensemble classifier with a Random Forest-based feature selection strategy. Three classes of Alzheimer’s disease are used in this work, namely AD, MCI (Mild Cognitive Impairment) and CN (Cognitive Normal). In the resulting analysis, the Binary classifications, i.e., AD vs. CN and MCI vs. CN, attained an accuracy (A_cc) of 99% in both cases. The class AD vs. MCI detection achieved an adequate accuracy (A_cc) of 91%. Furthermore, the Multi Class classification, i.e., AD vs. MCI vs. CN, achieved 96% (A_cc).

Diabetic retinopathy (DR) is one of the most common causes of visual impairment worldwide and requires reliable automated detection methods. Numerous research efforts have developed various conventional methods for early detection of DR. Research in the field of DR remains insufficient, indicating the potential for advances in diagnosis. In this paper, a hybrid model (HybridFusionNet) that integrates vision transformer (VIT) and attention processes is presented. It improves classification in the binary ($B_{cl}$) and multi-class ($M_{cl}$) stages by utilizing deep features from the DR stages. As a result, both the SAN and VIT models improve the recognition accuracy $\left(A_{cc}\right)$ in both stages.The HybridFusionNet mechanism achieves a competitive improvement in multi-stage and binary stages, which is $A_{cc}$ in $B_{cl}$ and $M_{cl}$, with 91% and 99%, respectively. This illustrates that this model is suitable for a better diagnosis of DR.

Amyotrophic lateral sclerosis is a neurodegenerative disorder that has a complex differential diagnosis that has not been elucidated due to the variety of clinical manifestations or specific biological markers. This review aims at studying the application of ensemble machine learning classifiers for enhancing the classification of ALS based on several models such as RandomForest, ExtraTrees, XGBoost, LightGBM, CatBoost, AdaBoost, Voting, and Stacking classifiers. Ensemble models offer enhanced performance in diagnosing ALS by utilizing diverse classification techniques with appropriate feature selection method. This study finds that while the Voting classifier produces comparatively inferior results, the ExtraTrees and CatBoost models perform better with adequate precision and recall and specificity. These studies on improvements in ensemble learning methods have the potential to greatly improve disease diagnosis and early identification, which will support individualized care plans for patient with ALS.