Biology subjects

Jobayer, M.

Publications and source records attributed to Jobayer, M..

2 recordsLinked to original sources

bioRxiv2025

Enhancing Medical Image Segmentation through Negative Sample Integration: A Study on Kvasir-SEG and Augmented Datasets

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with early and accurate detection being critical for improving patient outcomes. Automated image segmentation using deep learning has emerged as a transformative tool for identifying colorectal abnormalities in medical imaging. This study conducts a comparative analysis of three prominent deep learning architectures--U-Net, SegNet, and ResNet--for colorectal cancer image segmentation, evaluating their performance on a custom dataset comprising 1,800 images (1,000 polyp images from the Kvasir-SEG dataset and 800 polyp-free images from the WCE Curated Colon Dataset). The dataset was preprocessed to a uniform resolution of 256 x 256 pixels and partitioned into training, validation, and test sets. Quantitative and qualitative results demonstrate that U-Net outperforms SegNet and ResNet, achieving superior segmentation accuracy (validation accuracy of 0.95) and robustness, particularly when trained on datasets that include negative samples. SegNet showed the sign of overfitting and delivered unstable results, while ResNet struggled to generalize effectively. The integration of negative images improved specificity by decreasing false positive rates. Overall, the results demonstrate U-net as the most efficient in precise polyp segmentation, providing significant implications for robust diagnostic system development.

bioengineering↗

bioRxiv2024

Machine Learning to Predict Gut Microbiomes of Agricultural Pests

ContextWhile current efforts to control agricultural insect pests largely focus on the widespread use of insecticides, predicting microbiome composition can provide important data for creating more efficient and long-lasting pest control methods by analysing the pests food-digesting capacity and resistance to bacteria or viruses. AimsInstead of using computationally expensive techniques, we aim to investigate the dynamics of these microbiome compositions using metagenomic samples taken from fruit flies. MethodsIn this paper, we propose the three machine learning-based biological models. Firstly, we propose the intrafamilial successor prediction, which predicts the relative abundance of each bacterial family using the past four generations. Next, we propose our interfamilial quantitative prediction, where the model predicts the amount of a given bacterial family in each sample using the amount of all other bacteria present in the sample. Lastly. we propose our interfamilial qualitative prediction, which predicts the relative abundance of each bacterial family within a sample using binary information of all bacterial families. Key ResultsAll three models were tested against Least Angle Regression, Random Forest, Elastic-Net, and Lasso. The third approach exhibits promising results by applying a Random Forest with the lowest mean Coefficient of Variance of 1.25. ConclusionThe overall results of this study highlight how complex these dynamic systems are and demonstrate that more computationally efficient methods can characterise them quickly.

bioinformatics↗