This study explores the application of the YOLOv8 segmentation model in automated dental diagnostics through deep learning techniques, focusing on its effectiveness in detecting and classifying various dental features from X-ray images. The research emphasizes methodological rigor in dataset configuration, training with a pre-trained model, and employing advanced data augmentation strategies. Visualizations including bar charts and density plots illustrate the distribution and spatial characteristics of dental instances, highlighting prevalent categories like fillings and caries. The integration of deep learning in dental imaging not only enhances diagnostic precision but also supports personalized medicine by adapting diagnostic tools to individual patient data. This advancement holds promise for improving efficiency in disease detection and treatment planning, potentially transforming dental care delivery through enhanced automation and accuracy.

Cardiovascular diseases (CVDs) account for approximately 17.9 million deaths annually, making them a leading cause of mortality globally. As the population increases, the early detection and treatment of heart diseases are becoming more challenging. With advancements in machine learning (ML), models can now predict heart diseases effectively. In this study, we aim to predict whether a person has cardiovascular disease based on 11 clinical features from the UCI heart failure prediction dataset. We compare the performance of five different machine learning algorithms: Gradient Boosting (GB), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machine (SVM). Our results show that SVM is the most efficient technique with an accuracy of 94.56%, which can aid doctors in reducing heart disease-related fatalities.

Recent advancements in deep learning have revolutionized the field of dental diagnostics, particularly in the detection and characterization of dental caries from radiographic images. Dental caries, a widespread oral health issue globally, necessitates precise and timely detection methods to mitigate its impact on patient health. Traditional diagnostic methods relying on visual inspection and radiographic imaging are subjective and prone to variability. In contrast, deep learning techniques offer a promising solution by automating detection processes through learned patterns from extensive datasets. This study proposes a comprehensive methodology utilizing state-of-the-art deep learning, specifically employing the YOLOv8.2.21 model for automatic detection and segmentation of dental caries from radiographs.

Medical image segmentation is pivotal in modern healthcare for precise delineation of anatomical structures such as the gall bladder, kidney, liver, and spleen, facilitating accurate diagnostics and treatment planning. Deep learning, particularly convolutional neural networks (CNNs), has revolutionized this field by automating segmentation tasks with high accuracy and efficiency. This paper explores the application of deep learning techniques specific to these organs, addressing challenges like anatomical variability and image complexity. Advanced methodologies including attention mechanisms and contextual aggregation are integrated to enhance segmentation performance. The study leverages large annotated datasets to train and evaluate state-of-the-art segmentation models, aiming to improve clinical outcomes through reliable organ segmentation.

Cataracts, a leading cause of visual impairment and blindness worldwide, necessitate early detection and accurate classification for timely intervention. This study presents a deep learning-based approach to cataract classification, demonstrating remarkable performance. The model achieved a final training accuracy of approximately 90.88% and a final validation accuracy of 91.49%.

This study focuses on iris segmentation, a crucial aspect of iris recognition systems with implications in security and authentication. Leveraging Convolutional Neural Networks (CNNs), the study integrates preprocessing,

This study showcases Convolutional Neural Networks’ (CNNs) potential in detecting breast cancer from ultrasound images. Impressive accuracy highlights their value in precise and efficient diagnosis, paving the way for further research. The CNN’s remarkable accuracy, even with a small dataset of 1,500 images, suggests enhanced performance with larger and diverse datasets. CNNs for breast cancer detection via ultrasound offer a promising, potent approach to revolutionize medical imaging and diagnosis, boosting accuracy and reliability.

This study utilizes advanced techniques such as image processing, deep learning, and metaheuristics to detect lung cancer in its early stages. The proposed model achieves higher detection rates and outperforms expert physicians, demonstrating superior accuracy, sensitivity, and specificity. It also excels in lung segmentation tasks compared to a traditional CNN model.

This study evaluates the performance of a convolutional neural network (CNN) model for Alzheimer’s disease (AD) classification based on MRI image processing. The results show that after 22 epochs of training, the model achieved a validation accuracy of 80.61%. Furthermore, the model exhibited high precision (78.99%) and recall (30.55%) rates, along with a significant area under the curve (AUC) value of 86.05%. These findings suggest the potential of the CNN model in accurately identifying AD cases using MRI scans, emphasizing its effectiveness as a diagnostic tool for early detection and intervention in AD.