Cone Beam Computed Tomography (CBCT) has completely changed the way that bone disorders are diagnosed and treated, especially in the dental and maxillofacial domains. This article examines the diverse applications of computed tomography (CBCT) in the diagnosis and treatment of facial trauma, including mandibular, dentoalveolar, and other facial fractures, as well as bone abnormalities like dislocations and fractures. CBCT is useful for a wide range of dental conditions and greatly improves diagnostic accuracy in periodontics, orthodontics, endodontics, and dental implantology. Additionally, a comparison between CBCT and conventional imaging methods was conducted, emphasizing the latter’s inferior 3D imaging capabilities, allowing for more precise treatment planning and better patient outcomes with CBCT. Although CBCT has many benefits, it also has some drawbacks, such as requiring specific training for accurate interpretation, cost considerations, and a higher radiation exposure than with traditional dental X-rays. In order to optimize benefits and reduce risks, the conclusion highlights CBCT’s revolutionary influence on clinical practice while arguing for its prudent and responsible application.

Background: Breast cancer screening through mammography interpretation is crucial for early detection and improved patient outcomes. However, the manual classification of mammograms using the BIRADS (Breast Imaging-Reporting and Data System) remains challenging due to subtle imaging features, inter-reader variability, and increasing radiologist workload. Traditional computer-aided detection systems often struggle with complex feature extraction and contextual understanding of mammographic abnormalities. To address these limitations, this study proposes MammoViT, a novel hybrid deep learning framework that leverages both ResNet50’s hierarchical feature extraction capabilities and Vision Transformer’s ability to capture long-range dependencies in images. Methods: We implemented a multi-stage approach utilizing a pre-trained ResNet50 model for initial feature extraction from mammogram images. To address the significant class imbalance in our four-class BIRADS dataset, we applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for minority classes. The extracted feature arrays were transformed into non-overlapping patches with positional encodings for Vision Transformer processing. The Vision Transformer employs multi-head self-attention mechanisms to capture both local and global relationships between image patches, with each attention head learning different aspects of spatial dependencies. The model was optimized using Keras Tuner and trained using 5-fold cross-validation with early stopping to prevent overfitting. Results: MammoViT achieved 97.4% accuracy in classifying mammogram images across different BIRADS categories. The model’s effectiveness was validated through comprehensive evaluation metrics, including a classification report, confusion matrix, probability distribution, and comparison with existing studies. Conclusions: MammoViT effectively combines ResNet50 and Vision Transformer architectures while addressing the challenge of imbalanced medical imaging datasets. The high accuracy and robust performance demonstrate its potential as a reliable tool for supporting clinical decision-making in breast cancer screening.

MRI is a non-invasive imaging technique employed today in modern diagnostic medicine due to the fact it is capable of generating tissue architecture and function information with high image resolution without the use of ionizing radiation, unlike x-ray or CT scans. The advantages of MRI discussed in this review include better soft tissue contrast, the opportunity to perform imaging in different planes, and the ability to detect small changes in tissues, which helps to use MRI in many specialties, including cancer diagnosis and staging, as well as neurological and cardiovascular diseases. More particularly, this review aims to assess the contribution of MRI to the detection of liver cancer, especially HCC and ICC—the most frequent and aggressive types of pathology. Because of its high-resolution, MRI provides clear visualization of the small hepatic lesion and vascular mapping, which is crucial for early diagnosis and staging. It also reveals higher sensitivity and specificity than ultrasound and CT in identifying liver cancer dimensions and relations with system vasculature and a safer technique for patients who need many follow-up images. This is in addition to newer techniques that have been developed from MRI, which include the DWI, DCE-MRI, and MRE, all of which yield functional information concerning the perfusion of the tumor and the stiffness of the tissue, respectively, thus improving the diagnosis. Moreover, the application of artificial intelligence to MRI is improving lesion identification and cancer assessment, as well as patient outcome prediction, while relieving the burden of radiologists. Suggested improvements for future work include the combination of MRI with other diagnostic approaches, including circulating cell analysis and molecular imaging in managing liver cancer. Still, there is a limitation in MRI’s access globally, because scanners are expensive and unavailable in some parts of the world. Technological improvements and greater availability will extend MRI more as a valuable modality in the treatment of liver malignancies, more so for diagnosis and staging.

Breast cancer is a global health concern where early and accurate diagnosis is crucial. Mammogram scans provide detailed imaging but require expert interpretation, which is time-consuming. While deep learning shows promise in medical image analysis, the prevalence of imbalanced datasets in medical diagnosis hinders the development of accurate and reliable classification models. We propose a novel deep-learning framework for breast cancer classification from Mammogram scans. The framework addresses imbalanced data through a unique two-module pipeline incorporating the Synthetic Minority Over-sampling Technique (SMOTE). One module employs SMOTE on the entire dataset to balance class distribution. At the same time, the second separates a portion (20%) of the original imbalanced data for evaluation and applies SMOTE to the remaining 80%. The framework incorporates a blockwise Convolutional Neural Network (CNN), utilizing VGG16 preprocessing for input standardization and ResNet50 for feature extraction. A fully conntected classification model, consisting of multiple dense layers with batch normalization and dropout for regularization, was developed to assess the extracted features. The model architecture was iteratively refined to combat overfitting, with the final version incorporating three dense layers (128, 256, and 128 neurons) with dropout rates of 0.5. Our model achieved 99% accuracy on a balanced dataset and 90% on an imbalanced portion. The framework includes an interpretable visualization technique for randomly selected predictions across all classes. Our approach significantly improves diagnostic accuracy in breast cancer classification from Mammogram scans, effectively addressing the challenge of imbalanced data in medical image analysis. This work contributes to medical image analysis and computer-aided diagnosis. The proposed techniques for handling imbalanced data and providing interpretable results can be extended to improve diagnostic accuracy across various medical conditions.

In this paper, I present a comprehensive pipeline integrating a Fine-Tuned Convolutional Neural Network (FT-CNN) and a Residual-UNet (RUNet) architecture for the automated analysis of MRI brain scans. The proposed system addresses the dual challenges of brain tumor classification and segmentation, which are crucial tasks in medical image analysis for precise diagnosis and treatment planning. Initially, the pipeline preprocesses the FigShare brain MRI image dataset, comprising 3064 images, by normalizing and resizing them to achieve uniformity and compatibility with the model. The FT-CNN model then classifies the preprocessed images into distinct tumor types: glioma, meningioma, and pituitary tumor. Following classification, the RUNet model performs pixel-level segmentation to delineate tumor regions within the MRI scans. The FT-CNN leverages the VGG19 architecture, pre-trained on large datasets and fine-tuned for specific tumor classification tasks. Features extracted from MRI images are used to train the FT-CNN, demonstrating robust performance in discriminating between tumor types. Subsequently, the RUNet model, inspired by the U-Net design and enhanced with residual blocks, effectively segments tumors by combining high-resolution spatial information from the encoding path with context-rich features from the bottleneck. My experimental results indicate that the integrated pipeline achieves high accuracy in both classification (96%) and segmentation tasks (98%), showcasing its potential for clinical applications in brain tumor diagnosis. For the classification task, the metrics involved are loss, accuracy, confusion matrix, and classification report, while for the segmentation task, the metrics used are loss, accuracy, Dice coefficient, intersection over union, and Jaccard distance. To further validate the generalizability and robustness of the integrated pipeline, I evaluated the model on two additional datasets. The first dataset consists of 7023 images for classification tasks, expanding to a four-class dataset. The second dataset contains approximately 3929 images for both classification and segmentation tasks, including a binary classification scenario. The model demonstrated robust performance, achieving 95% accuracy on the four-class task and high accuracy (96%) in the binary classification and segmentation tasks, with a Dice coefficient of 95%.

Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment strategies and influences patient outcomes. This review comprehensively explores machine learning (ML) and deep learning (DL) models that enhance the accuracy and efficiency of brain tumor classification using medical imaging data, particularly Magnetic Resonance Imaging (MRI). As a noninvasive imaging technique, MRI plays a central role in detecting, segmenting, and characterizing brain tumors by providing detailed anatomical views that help distinguish various tumor types, including gliomas, meningiomas, and metastatic brain lesions. The review presents a detailed analysis of diverse ML approaches, from classical algorithms such as Support Vector Machines (SVM) and Decision Trees to advanced DL models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid architectures that combine multiple techniques for improved performance. Through comparative analysis of recent studies across various datasets, the review evaluates these methods using metrics such as accuracy, sensitivity, specificity, and AUC-ROC, offering insights into their effectiveness and limitations. Significant challenges in the field are examined, including the scarcity of annotated datasets, computational complexity requirements, model interpretability issues, and barriers to clinical integration. The review proposes future directions to address these challenges, highlighting the potential of multi-modal imaging that combines MRI with other imaging modalities, explainable AI frameworks for enhanced model transparency, and privacy-preserving techniques for securing sensitive patient data. This comprehensive analysis demonstrates the transformative potential of ML and DL in advancing brain tumor diagnosis while emphasizing the necessity for continued research and innovation to overcome current limitations and ensure successful clinical implementation for improved patient care.