Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively.

Segmenting breast tumors in ultrasound images is pivotal for computer-aided diagnosis (CAD) systems aimed at detecting breast cancer. This research contributes directly to the Sustainable Development Goal (SDG), good health and well-being by leveraging innovative deep learning technique to boost the accuracy of breast tumor segmentation. Achieving accurate segmentation is crucial for precise tumor size, shape, and location determination, which in turn facilitates subsequent tumor quantification and classification. However, this task presents challenges, particularly when dealing with small tumors. The complexity arises from factors such as speckle noise, variations in tumor shapes and sizes across patients, and the presence of tumor-like regions in the images. Although deep learning-based methods have shown remarkable success in biomedical image analysis, the current state-of-the-art approaches still struggle to achieve satisfactory performance in segmenting small breast tumors. In this paper, BUS (Breast Ultrasound) dataset was initially applied on state of-the art DeepLabV3 + architecture for the segmentation of breast lesions in ultrasound images. After that to focus on more informative features, a modified DeepLabV3 + has been proposed by integrating a CBAM module in encoder as well as decoder. At the end, comparative analysis of state-of-art DeepLabV3 + model and proposed modified DeepLabV3 + has also been presented in terms of performance metrics like Dice Coefficient, Intersection over Union, Precision, Recall and Specificity. The proposed model demonstrates outstanding performance across different evaluation metrics achieving precision, recall, specificity, dice coefficient and Intersection over Union (IoU) values of 0.974, 0.933, 0.997, 0.951, and 0.933. respectively.

White blood cells (WBCs) are essential for the human’s immune system alongside red blood cells and platelets. This study focuses on the identification of the fi White blood cells (WBCs) are essential for the human’s immune system alongside red blood cells and platelets. This study focuses on the identification of the fi

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The liver related diseases such as cirrhosis, fatty liver disease, and hepatocellular carcinoma raise significant health challenges in the world due to their increasing prevalence and their complexity in detection. This study features a deep learning-powered computer-aided diagnostic system that includes computed tomography (CT) images in the classification of liver diseases into groups of several classes. The task is to improve diagnosis quality through the implementation of all three pre-trained convolutional neural networks (CNNs) (ResNet50V2, DenseNet121, and MobileNetV2) to make multi-scale and multi-class imaging classification. All CNN models were thoroughly fine-tuned and evaluated at first. Transformer blocks were then added to every backbone to form a hybrid model. The models were trained and assessed on a CT liver dataset and performance measured based on precision, recall, F1-score and Matthews Correlation Coefficient. Findings show that there are marked enhancements with the addition of transformers especially in diagnosis of complex conditions, e.g., cirrhosis and fatty liver. The ensemble model, which was improved using the transformer, had the best overall accuracy of 97% which was higher than any single model. This study addresses the clinical benefits of CNNs, and transformers used together to classify liver diseases and provides suggestions about its application in clinical practice.