Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance

This study presents an advanced Convolutional Neural Network (CNN) architecture for ship classification from optical satellite imagery, significantly enhancing performance through the integration of the Convolutional Block Attention Module (CBAM) and additional architectural innovations. Building upon the foundational ResNet50 model, we first incorporated a standard CBAM to direct the model's focus towards more informative features, achieving an accuracy of 87% compared to the baseline ResNet50's 85%. Further augmentations involved multi-scale feature integration, depthwise separable convolutions, and dilated convolutions, culminating in the Enhanced ResNet Model with Improved CBAM. This model demonstrated a remarkable accuracy of 95%, with precision, recall, and f1-scores all witnessing substantial improvements across various ship classes. The bulk carrier and oil tanker classes, in particular, showcased nearly perfect precision and recall rates, underscoring the model's enhanced capability in accurately identifying and classifying ships. Attention heatmap analyses further validated the improved model's efficacy, revealing a more focused attention on relevant ship features, regardless of background complexities. These findings underscore the potential of integrating attention mechanisms and architectural innovations in CNNs for high-resolution satellite imagery classification. The study navigates through the challenges of class imbalance and computational costs, proposing future directions towards scalability and adaptability in new or rare ship type recognition. This research lays a groundwork for the application of advanced deep learning techniques in the domain of remote sensing, offering insights into scalable and efficient satellite image classification.

Deep Residual CNN for Multi-Class Chest Infection Diagnosis

The advent of deep learning has significantly propelled the capabilities of automated medical image diagnosis, providing valuable tools and resources in the realm of healthcare and medical diagnostics. This research delves into the development and evaluation of a Deep Residual Convolutional Neural Network (CNN) for the multi-class diagnosis of chest infections, utilizing chest X-ray images. The implemented model, trained and validated on a dataset amalgamated from diverse sources, demonstrated a robust overall accuracy of 93%. However, nuanced disparities in performance across different classes, particularly Fibrosis, underscored the complexity and challenges inherent in automated medical image diagnosis. The insights derived pave the way for future research, focusing on enhancing the model's proficiency in classifying conditions that present more subtle and nuanced visual features in the images, as well as optimizing and refining the model architecture and training process. This paper provides a comprehensive exploration into the development, implementation, and evaluation of the model, offering insights and directions for future research and development in the field.