Abstract: cardiovascular diseases (CVD) are found to be rampant in the populace leading to fatal death. Worldwide, the majority of people are suffering from CVD. However, the performance of current deep learning models is heavily reliant on the availability of large, well-annotated training datasets, which are frequently difficult to obtain in the medical domain due to privacy concerns and labeling costs. Inadequate or imbalanced data might cause biased predictions, lowering the model's reliability in detecting fewer common arrhythmias and possibly leading to diagnostic errors in essential cases. In this study a novel Machine learning-based CAD-SULOR model is proposed for CVD using support vector machine (SVM) and logistic regression (LR). The input signal is pre-processed using Least mean square (LMS) algorithm to reduce the noise and enhance the signal. Discrete Wavelet Transformation (DWT) is used to extract the features from the ECG signal. The SVM, and LR are utilized to improve the accuracy and classify the CVD, such as normal and abnormal. The performance of the CAD-SULOR approaches was assessed using the metrics such as F1 score, specificity, recall, accuracy, and precision. The CAD-SULOR approach achieves a high accuracy of 99.26% for cloth retrieval. The CAD-SULOR the accuracy 8.36%, 2.84% and 0.39% better than DDR-Net [13], MobileNet [14], and LSTM [16] respectively.

Abstract: Elderly health requires continuous monitoring to enable early detection of neurodegenerative conditions. This study positions Parkinson’s disease (PD) detection as a core function of an IoT (Internet of Thing)-based elder monitoring system that collects voice and sensor data for remote analysis. Initially, voice signals are denoised using an adaptive wavelet thresholding (AWT) method, which effectively suppresses background noise and enhances the image. The proposed PD-LSTM can be integrated as the deep learning decision module in an IoT-based elder monitoring framework, enabling automated, continuous monitoring and alerting for caregivers and clinicians. Mel Frequency Cepstral Coefficients (MFCC) are used as a feature extraction technique to produce discriminant features, and a sparse autoencoder is used to extract the features of the voice signal (VS). Finally, the Bi-directional LSTM (BDLSTM) used to classify the PD such as normal, and Parkinson. The proposed PD-LSTM approach not only enhances Parkinson’s detection accuracy but also forms a potential component of an IoT-enabled elder monitoring ecosystem, providing continuous and intelligent healthcare assistance. The performance of the PD-LSTM approaches was assessed using the metrics such as F1 score, specificity, recall, accuracy, and precision. The PD-LSTM approach achieves a high accuracy of 99.22% for PD. The PD-LSTM improves the accuracy range of 10.47%, 3.19% and 11.85% better than ZFNet-LHO-DRN, FB-DNN, and Ma-ST-DGN respectively

Abstract: Autism Spectrum Disorder (ASD) is a neurological disorder which might have a lifelong impact on the language learning, speech, cognitive, and social skills of an individual. However, the current ViT-ARDNet-LSTM model is mostly dependent on standardized, high-quality images and scans, which might not always be accessible in a variety of medical facilities. The generalizability and robustness of the model in real-world medical environments may also be limited by how differently images and scans acquisition techniques, scanner types, and image resolutions vary throughout institutions. In this paper, a novel DL-based AUSD-XVGG model is proposed for ASD classification using Xception and VGG16. Initially, the input images are preprocessed using log transformation and normalization to enhance the image and remove the noise. MobileNetV2 is used for feature extraction of ASD images to extract the features. The hybrid classification is Xception and VGG16, which captures the depth and spatial features of facial expressions. The AUSD-XVGG approach classifies as autism and normal. The performance of the AUSD-XVGG approaches was assessed using the metrics such as F1 score, specificity, recall, accuracy, and precision. The AUSD-XVGG approach achieves a high accuracy of 99.07% for ASD. The AUSD-XVGG improves the accuracy range of 26.09%, 5.35% and 2.96% better than DNN, ViT-ARDNet-LSTM, and IMFRCNN respectively

Abstract: Lung cancer is among the world's leading causes of death and its accurate and early diagnosis is critical for effective treatment planning. However, lung cancer often presents subtle early symptoms, making early detection challenging, and its diagnosis relies heavily on imaging techniques that can sometimes miss small or irregular nodules. To overcome these challenges a novel MLSIA-LC has been proposed deep learning-based Multi-Level Self and Inter-Attention (MLSIA) Network for the classification of lung cancer subtypes using CT and PET scan images. Then the segmentation of lung nodules using the Attention U-Net model. Advanced texture patterns, namely Directional Hexagonal Inter Mixed Pattern (DHIMP) and Directional Hexagonal Inter-Intra Mixed Pattern (DHIIMP), are extracted from segmented regions to capture unique spatial features from both CT and PET images. These patterns are fed into a parallel network architecture comprising three pathways, each with two levels of Multi-Level Self-Attention (MLSA) networks and Inter-Attention layers, followed by dropout mechanisms to enhance feature. The experimental results of MLSIA network achieves higher classification performance compared to existing methods, particularly with the DHIIMP texture pattern. The proposed model achieved an accuracy of 92.1% and an F1 score of 90%, outperform effectively leveraging inter- and intra-pattern information

Abstract: Huntington's disease (HD) is a neurological condition caused by a trinucleotide repli-cation extension in the Huntington gene. The degeneration of the corticobasal gan-glia's white matter networks causes progressive impairment of motor, cognitive, and neuropsychiatric functioning. Magnetic resonance imaging (MRI) is increasingly be-ing utilized to measure changes in the brain during the early stages of HD, as gene carriers for the disease demonstrate significant neuronal loss until the conclusion of the illness. The absence of tagged data is a significant challenge, particularly in the early phases of the illness. To overcome this, the study's proposed strategy is applied to MRI imaging of Huntington Disease. MRI pictures are pre-processed to reduce noise and improve image quality using normalization. ResNext is a deep learning system that uses MRI pictures to accurately restore hierarchical structures and ex-tract features. After extracting the features, the SNN network is utilized to classify the detection. Finally, by classifying MRI scans as normal, far, mild, near, or HD, the model improves the accuracy of Huntington Disease detection. The proposed HD-RSNN detection accuracy of this optimized system measured at 98.83%. The HD-RSNN datasets were used to validate the suggested approach, yielding accuracy val-ues of 1.81%, 2.40%, and 0.35%, respectively.