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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

Lung and colon cancers are common types of cancer with significant fatality rates. Early identification considerably improves the odds of survival for those suffering from these diseases. Histopathological image analysis is crucial for detecting cancer by identifying morphological anomalies in tissue samples. Regulations such as the HIPAA and GDPR impose considerable restrictions on the sharing of sensitive patient data, mostly because of privacy concerns. Federated learning (FL) is a promising technique that allows the training of strong models while maintaining data privacy. The use of a federated learning strategy has been suggested in this study to address privacy concerns in cancer categorization. To classify histopathological images of lung and colon cancers, this methodology uses local models with an Inception-V3 backbone. The global model is then updated on the basis of the local weights. The images were obtained from the LC25000 dataset, which consists of five separate classes. Separate analyses were performed for lung cancer, colon cancer, and their combined classification. The implemented model successfully classified lung cancer images into three separate classes with a classification accuracy of 99.867%. The classification of colon cancer images was achieved with 100% accuracy. More significantly, for the lung and colon cancers combined, the accuracy reached an impressive 99.720%. Compared with other current approaches, the proposed framework showed an improved performance. A heatmap, visual saliency map, and GradCAM were generated to pinpoint the crucial areas in the histopathology pictures of the test set where the models focused in particular during cancer class predictions. This approach demonstrates the potential of federated learning to enhance collaborative efforts in automated disease diagnosis through medical image analysis while ensuring patient data privacy.

Pathologists have depended on their visual experience to assess tissue structures in smear images, which was time-consuming, error-prone, and inconsistent. Deep learning, particularly Convolutional Neural Networks (CNNs), offers the ability to automate this procedure by recognizing patterns in tissue images. However, training these models necessitates huge amounts of labeled data, which can be difficult to come by due to the skill required for annotation and the unavailability of data, particularly for rare diseases. This work introduces a new semi-supervised method for tissue structure semantic segmentation in histopathological images. The study presents a CNN based teacher model that generates pseudo-labels to train a student model, aiming to overcome the drawbacks of conventional supervised learning approaches. Self-supervised training is used to improve the teacher model’s performance on smaller datasets. Consistency regularization is integrated to efficiently train the student model on labeled data. Further, the study uses Monte Carlo dropout to estimate the uncertainty of proposed model. The proposed model demonstrated promising results by achieving an mIoU score of 0.64 on a public dataset, highlighting its potential to improve segmentation accuracy in histopathological image analysis.

The increasing incidence of gastric cancer and the complexity of histopathological image interpretation present significant challenges for accurate and timely diagnosis. Manual assessments are often subjective and time-intensive, leading to a growing demand for reliable, automated diagnostic tools in digital pathology. This study proposes a hybrid deep learning approach combining convolutional neural networks (CNNs) and Transformer-based architectures to classify gastric histopathological images with high precision. The model is designed to enhance feature representation and spatial contextual understanding, particularly across diverse tissue subtypes and staining variations. Three publicly available datasets—GasHisSDB, TCGA-STAD, and NCT-CRC-HE-100 K—were utilized to train and evaluate the model. Image patches were preprocessed through stain normalization, augmented using standard techniques, and fed into the hybrid model. The CNN backbone extracts local spatial features, while the Transformer encoder captures global context. Performance was assessed using fivefold cross-validation and evaluated through accuracy, F1-score, AUC, and Grad-CAM-based interpretability. The proposed model achieved a 99.2% accuracy on the GasHisSDB dataset, with a macro F1-score of 0.991 and AUC of 0.996. External validation on TCGA-STAD and NCT-CRC-HE-100 K further confirmed the model’s robustness. Grad-CAM visualizations highlighted biologically relevant regions, demonstrating interpretability and alignment with expert annotations. This hybrid deep learning framework offers a reliable, interpretable, and generalizable tool for gastric cancer diagnosis. Its superior performance and explainability highlight its clinical potential for deployment in digital pathology workflows.

Reliability in diagnosing and treating brain tumors depends on the accurate grading of histopathological images. However, limited scalability, adaptability, and interpretability challenge current methods for frequently grading brain tumors to accurately capture complex spatial relationships in histopathological images. This highlights the need for new approaches to overcome these shortcomings. This paper proposes a comprehensive hybrid learning architecture for brain tumor grading. Our pipeline uses complementary feature extraction techniques to capture domain-specific knowledge related to brain tumor morphology, such as texture and intensity patterns. An efficient method of learning hierarchical patterns within the tissue is the 2D-3D hybrid convolution neural network (CNN), which extracts contextual and spatial features. A vision transformer (ViT) additionally learns global relationships between image regions by concentrating on high-level semantic representations from image patches. Finally, a stacking ensemble machine learning classifier is fed concatenated features, allowing it to take advantage of the individual model’s strengths and possibly enhance generalization. Our model’s performance is evaluated using two publicly accessible datasets: TCGA and DeepHisto. Extensive experiments with ablation studies and cross-dataset evaluation validate the model’s effectiveness, demonstrating significant gains in accuracy, precision, and specificity using cross-validation scenarios. In total, our brain tumor grading model outperforms existing methods, achieving an average accuracy, precision, and specificity of 97.1%, 97.1%, and 97.0%, respectively, on the TCGA dataset, and 95%, 94%, and 95% on DeepHisto dataset. Reported results demonstrate how the suggested architecture, which blends deep learning (DL) with domain expertise, can achieve reliable and accurate brain tumor grading.

Breast cancer remains a major global health concern and is the leading cause of cancer-related deaths among women worldwide. It is estimated that by 2030, its incidence and mortality rates will rise due to population growth, aging, and lifestyle changes. Although histopathological examination is considered the gold standard for diagnosis, manual assessment is often time-consuming, subjective, and—particularly in the case of complex, multidimensional cancer classifications—prone to inter-observer variability. These limitations underscore the need for accurate, automated, and computationally efficient diagnostic systems to aid in clinical decision-making. To address these challenges, this study introduces BC-SwinNet—a hybrid deep learning framework that integrates the Conditional Swin Transformer (ConSwinTras), the Multi-Objective Elk Herd Optimization (MEHO) algorithm, and a Layered Attention-based Convolutional Neural Network (CA-CNN) to classify multiple breast cancer subtypes using histopathological images. ConSwinTras extracts hierarchical and contextual representations from tissue images, while MEHO is utilized to select subsets of high-resolution features, thereby reducing dimensionality and enhancing prediction performance. The CA-CNN module focuses on diagnostically relevant regions through layer-wise attention algorithms and performs the final sample analysis and classification. The proposed BC-SwinNet model was evaluated on two benchmark datasets—BreakHis and BACH—achieving classification accuracies of 99.91% and 99.854%, respectively. Experimental results demonstrate that the proposed framework outperforms numerous existing methods in terms of classification performance while maintaining computational efficiency. These findings suggest that BC-SwinNet offers a robust and efficient approach for automated breast cancer diagnosis and holds the potential to enhance diagnostic support systems in clinical practice.

The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature. This paper presents a two-stage segmentation method to obtain cellular structures in high-dimensional histopathological images of renal cell carcinoma. First, the image is segmented to superpixels with simple linear iterative clustering (SLIC) method. Then, the obtained superpixels are clustered by the state-of-the-art clustering-based segmentation algorithms to find similar superpixels that compose the cell nuclei. Furthermore, the comparison of the global clustering-based segmentation methods and local region-based superpixel segmentation algorithms are also compared. The results show that the use of the superpixel segmentation algorithm as a pre-segmentation method improves the performance of the cell segmentation as compared to the simple single clustering-based segmentation algorithm. The true positive ratio (TPR), true negative ratio (TNR), F-measure, precision, and overlap ratio (OR) measures are utilized as segmentation performance evaluation. The computation times of the algorithms are also evaluated and presented in the study.

This research addresses the challenge of automating skin disease diagnosis using dermatoscopic images. The primary issue lies in accurately classifying pigmented skin lesions, which traditionally rely on manual assessment by dermatologists and are prone to subjectivity and time consumption. By integrating a hybrid CNN-DenseNet model, this study aimed to overcome the complexities of differentiating various skin diseases and automating the diagnostic process effectively. Our methodology involved rigorous data preprocessing, exploratory data analysis, normalization, and label encoding. Techniques such as model hybridization, batch normalization and data fitting were employed to optimize the model architecture and data fitting. Initial iterations of our convolutional neural network (CNN) model achieved an accuracy of 76.22% on the test data and 75.69% on the validation data. Recognizing the need for improvement, the model was hybridized with DenseNet architecture and ResNet architecture was implemented for feature extraction and then further trained on the HAM10000 and PAD-UFES-20 datasets. Overall, our efforts resulted in a hybrid model that demonstrated an impressive accuracy of 95.7% on the HAM10000 dataset and 91.07% on the PAD-UFES-20 dataset. In comparison to recently published works, our model stands out because of its potential to effectively diagnose skin diseases such as melanocytic nevi, melanoma, benign keratosis-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma, all of which rival the diagnostic accuracy of real-world clinical specialists but also offer customization potential for more nuanced clinical uses.

Histopathological images stained with hematoxylin and eosin (H&E) are crucial for cancer diagnosis and prognosis. However, color variations caused by differences in tissue preparation and scanning devices can lead to data distribution discrepancies, adversely affecting the performance of downstream algorithms in tasks like classification, segmentation, and detection. To address these issues, stain normalization methods have been developed to standardize color distributions across images from various sources. Recent advancements in deep learning-based stain normalization methods have shown significant promise due to their minimal preprocessing requirements, independence from reference templates, and robustness. This review examines 115 publications to explore the latest developments in this field. We first outline the evaluation metrics and publicly available datasets used for assessing stain normalization methods. Next, we systematically review deep learning-based approaches, including supervised, unsupervised, and self-supervised methods, categorizing them by core technologies and analyzing their contributions and limitations. Finally, we discuss current challenges and future directions, aiming to provide researchers with a comprehensive understanding of the field, promote further development, and accelerate the progress of intelligent cancer diagnosis.