Perception and Localization of Macular Degeneration Applying Convolutional Neural Network, ResNet and Grad-CAM

A well-known retinal disease that sends blurry visions to the affected patients is Macular Degeneration. This research is based on classifying the healthy and macular degeneration fundus by localizing the affected region of the fundus. A CNN architecture and CNN with ResNet architecture (ResNet50, ResNet50v2, ResNet101, ResNet101v2, ResNet152, ResNet152v2) as the backbone are used to classify the two types of fundus. The data are split into three categories including (a) Training set is 90% and Testing set is 10% (b) Training set is 80% and Testing set is 20%, (c) Training set is 50% and Testing set is 50%. After the training, the best model has been selected from the evaluation metrics. Among the models, CNN with a backbone of ResNet50 performs best which gives the training accuracy of 98.7% for 90% train and 10% test data split. With this model, we have performed the Grad-CAM visualization to get the region of the affected area of the fundus.

Adaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced Analysis of Kidney Abnormalities in CT Scan Images using ensemble based Machine Learning Approach

The shortage of nephrologists and the growing public health concern over renal failure have spurred the demand for AI systems capable of autonomously detecting kidney abnormalities. Renal failure, marked by a gradual decline in kidney function, can result from factors like cysts, stones, and tumors. Chronic kidney disease may go unnoticed initially, leading to untreated cases until they reach an advanced stage. The dataset, comprising 12,427 images from multiple hospitals in Dhaka, was categorized into four groups: cyst, tumor, stone, and normal. Our methodology aims to enhance CT scan image quality using Cropping, Resizing, and CALHE techniques, followed by feature extraction with our proposed Adaptive Local Binary Pattern (A-LBP) feature extraction method compared with the state-of-the-art local binary pattern (LBP) method. Our proposed features fed into classifiers such as Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbor, and SVM. We explored an ensemble model with soft voting to get a more robust model for our task. We got the highest of more than 99% in accuracy using our feature descriptor and ensembling five classifiers (Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbor, Support Vector Machine) with the soft voting method.

Real-Time Detection and Analysis of Vehicles and Pedestrians using Deep Learning

Computer vision, particularly vehicle and pedestrian identification is critical to the evolution of autonomous driving, artificial intelligence, and video surveillance. Current traffic monitoring systems confront major difficulty in recognizing small objects and pedestrians effectively in real-time, posing a serious risk to public safety and contributing to traffic inefficiency. Recognizing these difficulties, our project focuses on the creation and validation of an advanced deep-learning framework capable of processing complex visual input for precise, real-time recognition of cars and people in a variety of environmental situations. On a dataset representing complicated urban settings, we trained and evaluated different versions of the YOLOv8 and RT-DETR models. The YOLOv8 Large version proved to be the most effective, especially in pedestrian recognition, with great precision and robustness. The results, which include Mean Average Precision and recall rates, demonstrate the model's ability to dramatically improve traffic monitoring and safety. This study makes an important addition to real-time, reliable detection in computer vision, establishing new benchmarks for traffic management systems.