Hyperspectral imagery, capturing information beyond the visible spectrum, is essential for many remote sensing applications. This paper introduces a novel approach utilizing a 3D Convolutional Neural Network (CNN) enhanced with self-attention mechanism to address the limitations of traditional architectures in hyperspectral image classification. The main problem tackled is the effective extraction of both spatial and spectral features from complex hyperspectral datasets. Our proposed model simultaneously analyzes spatial and spectral dimensions with improved accuracy and adaptability through self-attention. We systematically evaluate the performance of 1D CNN, 2D CNN, and 3D CNN models, alongside Multi-Column Neural Network (MCNN) and Hybrid Spectral Network (Hybrid SN) models, comparing them to our proposed approach. The evaluation, conducted on the Indian Pines and Salinas datasets, highlights significant improvements in classification accuracy and adaptability. Specifically, the proposed 3D CNN with self-attention achieves accuracy values of 98.45% and 91.32% on the Indian Pines and Salinas datasets, respectively. This paper provides critical insights for data scientists and remote sensing experts, contributing to informed decision-making in selecting CNN models tailored to specific application requirements and advancing the field of hyperspectral image classification (HSIC). Furthermore, our approach shows improved handling of the complex spectral variability and spatial heterogeneity inherent in hyperspectral data.

Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. This work leveraged how the convolutional model and pooling layers work to improve and reduce overfitting, despite challenges in training the numerical dataset. VGG was preferably chosen as a hidden layer as it has a more diverse, deeper, and simpler architecture with better performance when dealing with larger datasets. It consumes less memory and training time. A comparative analysis was performed using machine learning and neural network algorithm techniques. Performance metrics such as accuracy, error rate, precision, recall, F1 score, sensitivity, specificity, kappa statistics, ROC, and RMSE were experimented with and compared. The accuracy was 100% for VGG16 and VGG19 and 98.20% for Alex Net. The precision was 99.9% for VGG16, 96.6% for VGG19, and 100% for Alex Net; the recall values were 99.9% for all three cases of VGG16, VGG19, and Alex Net; and the sensitivity metric was 96.8% for VGG16, 97.9% for VGG19, and 98.7% for Alex Net, which has outperformed when compared with the existing approaches for the classification of Alzheimer’s disease. This research contributes to the advancement of predictive knowledge, leading to future empirical evaluation, experimentation, and testing in the biomedical field.

Cancer diagnosis and treatment has a great significance due to the prevalent episodes of the diseases, high death rate and reappearance after treatment. On the world scale, cancer stands in the fifth position which causes death. Among the various cancers, liver cancer stands in the third position. Liver cancer is generally diagnosed by three different test like blood test, image test and biopsy. To make the task of detecting the liver cancer simpler, less time consuming, an effective and efficient approach is adopted for the same. In this research a computer aided diagnostic system for detecting liver cancer is put forward. The proposed detection methodology makes use of MRI, CT and USG scan imagery. K-means clustering technique is adopted so as to segment the images in order to capture the region of interest. Later, Haar wavelet transform is considered to compute the threshold values for the region of interest. The experiment put forth gave an average accuracy of 82% besides reducing the time complexity and computational complexity of the test.

Cancer has been a plague in our society since the dawn of recorded history. The radical surgical resection represents the only chance for cure but, unfortunately it is possible in only 15% of patients. Even at experienced centres the 5-year survival rates for the most favourable patients who undergo resection and adjuvant therapy are less than 20%. In this paper, a methodology is proposed for identifying the tumor size and based on the size, the stage of the cancer is identified. The methodology involves scanned images captured at various locations of the human body which are collected from different diagnostic labs. Based on Region Growing Algorithm the segmentation is carried out to analyze the tumor part via which the size of the tumor and the stage at which the tumor relies is empirically calculated.