In many fields, data-driven decision making has become essential due to machine learning (ML), which provides insights that improve productivity and quality of life. A basic machine learning approach called clustering helps find comparable data points. Clustering plays a critical role in the identification of patient subgroups and the customisation of treatment in the context of tuberculosis (TB) research. While prior studies have recognized its utility, a comprehensive comparative analysis of multiple clustering methods applied to TB data is lacking. Using TB data, this study thoroughly assesses and contrasts four well-known machine learning clustering algorithms: spectral clustering, DBSCAN, hierarchical clustering, and k-means. To evaluate the quality of a cluster, quantitative measures such as the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index are utilised. The results provide quantitative insights that enhance comprehension of clustering and guide future research.

Colorectal cancer is the third most prevalent type of cancer globally, and it typically progresses unnoticed, making early detection via effective screening methods crucial. This study presents the TASPP-UNet, an advanced deep learning model that integrates Atrous Spatial Pyramid Pooling (ASPP) blocks and a ResNet-50 encoder to enhance polyp boundary delineation accuracy in colonoscopy images. We utilized augmented datasets from Kvasir-SEG and CVC Clinic-DB, which included up to 2000 images, to enrich the training examples’ variability. The TASPP-UNet achieved a superior IOU of 0.9276, compared to 0.9128 by the ResNet50-UNet and 0.8607 by the standard U-Net, demonstrating its efficacy in precise segmentation tasks. Notably, this model exhibited impressive computational efficiency with a processing speed of 151.1 frames per second (FPS), underscoring its potential for real-time clinical applications aimed at early and accurate colorectal cancer detection. This performance highlights the model’s capability not only to improve diagnostic accuracy but also to enhance clinical workflows, potentially leading to better patient outcomes.

This paper investigates the application of convolutional neural networks (CNNs), particularly the UNet model architecture, to improve the accuracy of breast cancer tumor segmentation in ultrasound images. Accurate identification of breast cancer is essential for effective patient treatment. However, ultrasound images, often contain noise and artifacts, which can complicate the task of tumor segmentation. Therefore, to highlight the most robust architecture, modifications were made to the original set, including the addition of noise and fuzziness. In this study, a comparative study of five different variants of UNet models (UNet, Attention UNet, UNet++, Dense Inception UNet and Residual UNet) was conducted on a diverse set of ultrasound images with different breast tumors. Using consistent training methods and techniques of augmentation and adding noise to the data, an improvement in segmentation accuracy was highlighted when using the Dense Inception UNet architecture. The results have potential practical applications in the field of medical diagnosis and can assist medical professionals in automatic tumor segmentation in breast cancer ultrasound images. This study highlights the improvement of segmentation accuracy by introducing dense induction into the UNet architecture. Importantly, the Dice coefficient, a key segmentation metric, improved markedly, increasing from 0.973 to 0.976 after data augmentation. The results of the study offer promise to the medical community by offering a more accurate and reliable approach to segmenting breast cancer lesions on ultrasound images. The findings can be implemented in clinical practice to assist radiologists in early cancer diagnosis.

The prevalence of diabetes in Kazakhstan has reached epidemic proportions, and this disease is becoming a major financial burden. In this research, regression analysis methods were employed to build models for predicting the number of diabetic patients in Kazakhstan in 2019, as this should aid the costing and policy-making performed by medical institutions and governmental offices regarding diabetes prevention and treatment strategies.