Breast cancer remains a major contributing factor to death from cancer in women worldwide, requiring accurate and prompt diagnosis. While many deep learning approaches have shown a lot of potential in automated histopathological image classification, existing methods often struggle with capturing fine-grained features and handling the high intra-class variability present in breast tissue images. This paper proposes TFEA-Net, a Triplet Feature Enhancement Attention Network that addresses these challenges through a novel attention mechanism combined with multi-scale feature extraction. The proposed architecture integrates a Swin Transformer backbone with a custom-designed model module that operates spatial and channel attention mechanisms in parallel. Multi-scale feature recognition is carried out through parallel dilated convolutions employing multiple dilation rates, allowing the network to identify discriminative features across different receptive field sizes. We evaluated TFEA-Net on the BreakHis dataset at different zoom levels (40X, 100X, 200X and 400X), achieving accuracies of 99%, 98%, 96% and 95%, respectively. The experimental results demonstrate that TFEA-Net outperforms the existing methods.

Early detection of colorectal polyps is crucial in preventing colorectal cancer. While convolutional neural networks (CNN) have achieved overwhelming success in classifying medical images, they face challenges in accurately categorizing different types of polyps from colonoscopy images due to their varying sizes and shapes. To address these issues, we introduce a wavelet pooling-based multiscale contextual fusion network called WMCF-Net for efficient polyp classification. The WMCF-Net comprises a sequence of multiscale contextual fusion (MCF) blocks and different pooling layers in its three branches. The MCF block aids in capturing contextual features at various scales. The features from each branch are finally fused to learn more detailed information from colonoscopy images comprehensively. The network also leverages wavelet pooling for feature map size reduction while preserving more detailed spatial information. The WMCF-Net model is evaluated on four benchmark datasets: Kvasir-V2 and CP-CHILD dataset for poly vs. no polyp classification, and Piccolo and Endoscopic dataset for adenoma vs. hyperplastic classification. The proposed model obtains a classification accuracy of 98.00% and 99.75% on the Kvasir-V2 and CP-CHILD datasets, respectively. While an accuracy of 99.80%, 100.00%, and 93.14% is achieved on set-1 and set-2 of the Endoscopic dataset and Piccolo datasets, respectively. The ablation studies and comparisons with state-of-the-art methods further demonstrate its efficacy. The WMCF-Net is end-to-end learnable, lightweight, and can hence be efficiently utilized in real-time clinical applications.

In colorectal cancer analysis, polyps segmentation is one of the crucial task where encoder–decoder style architecture plays a significant role as a base model. However, it suffers from the issue of loosing contextual and spatial information, which ultimately results poor performance. To address these issues, we introduce a residual multi-scale semantic polyp segmentation approach named MSPolypNet for efficient polyps segmentation. MSPolypNet improves contextual understanding and preserves spatial information by integrating innovative modules namely, residual multi-path atrous spatial pyramid pooling block (RMAB) and cross-spatial attention (CSA) enriched with dilated convolutions to capture intricate details across varying scales while maintaining computational efficiency. The proposed model was rigorously trained and tested on six independent datasets. Additionally, to assess cross-dataset performance, two separate datasets not used during training were exclusively utilized for testing. MSPolypNet achieved Dice Score and mIoU scores of 90.86% and 88.75% on the Kvasir-Seg and 94.92% and 90.56% on the CVC-ClinicDB, demonstrating MSPolypNet robustness and efficiency. Experimental results show a substantial improvement in segmentation accuracy, highlighting the potential of the proposed model to become a new benchmark for polyp segmentation. Its fewer parameters, compared to other models, provide an advantage for using our MSPolypNet in reliable -time clinical evaluations.

Polyp segmentation is essential for the early detection and treatment of colorectal cancer using colonoscopy images. The computer-aided diagnosis (CAD) systems assist colonoscopists in improving the diagnosis process and reducing missed detection rates. One of the major challenges in polyp segmentation is the variability in the size and shape of the polyps that affect the segmentation performance. Another significant difficulty is distinguishing between the polyps and the surrounding tissues. This research proposes an encoder–decoder based architecture that incorporates a Global Cross-Dimensional Attention (GCDA) module to capture complex patterns, facilitating cross-dimensional interactions with a global perspective. This enhances segmentation accuracy by providing broader contextual information, effectively addressing the issue of variability in polyp size. Additionally, the feature extraction is further enhanced using a Scale-Aware Feature Extraction (SAFE) to address the issue of polyp shape and size variability more effectively. The proposed method combines Local-Global Features Fusion (LGFF), which effectively differentiates polyps from the surrounding mucosa. The proposed architecture was assessed on two benchmark datasets and achieved a mean Intersection over Union (mIoU) of 0.952 and 0.893 on the CVC-ClinicDB and the Kvasir-SEG dataset, respectively. The proposed model outperformed nine state-of-the-art (SOTA) models in terms of performance. The proposed model demonstrated strong performance in the generalizability tests involving cross-dataset evaluation along with two additional datasets: Hyper Kvasir and ETIS Larib dataset.

Detecting and removing polyps during colonoscopy in an early stage plays a crucial role in preventing colorectal cancer. Deep learning techniques have shown promise in automatically identifying polyps. However, the most current methods often prioritize accuracy over computational efficiency, which makes them hard to use in clinical settings with limited resources. This study introduces DYOLO, an improved deformable YOLOv7 architecture that effectively combines detection performance with computational efficiency for real-time polyp detection. The proposed method incorporates channel and spatial attention into the backbone network to improve discriminative feature learning, allowing the model to concentrate on essential polyp features while minimizing background noise. Deformable convolutions are integrated into the neck architecture to facilitate adaptive receptive field adjustment, thereby enhancing the model's capacity to address various polyp morphologies observed in clinical settings. To evaluate the robustness of the DYOLO model, comprehensive testing was conducted using the Kvasir-SEG dataset, demonstrating that DYOLO achieves superior performance with 96.35% precision, 93.10% recall, and 95.8% mAP @ 0.5, outperforming existing methods.

Early detection of colorectal cancer (CRC) can improve the survival rate with a better treatment plan. It is essential to classify the pathological findings of the gastrointestinal (GI) tract to determine GI diseases, such as ulcers or cancerous polyps. The diagnosis of a pathological finding is performed using an automated computer-aided diagnosis (CAD) system. It is always a challenging issue between computational complexity and performance accuracy when developing a CNN model. The proposed work focuses on the development of an improved MobileNet with reduced computational complexity and better accuracy, aiming at deployment in resource-constrained devices such as FPGAs. The proposed MobileNet is an improved version of MobileNet-V2 with efficient use of depthwise separable convolution, along with a multi-kernel feature fusion mechanism to improve performance. The reduction in the layers is compensated with cross-bottleneck feature fusion to improve performance with lower computation. The performance of the proposed model was evaluated with the Kvasir dataset for the pathological finding, and it achieved a classification accuracy of 95.6% with a significant reduction in computational parameters. The hardware efficiency was validated by deploying the model on an FPGA, giving a throughput of 457 frames per second with a better accuracythroughput trade-off. The performance of the proposed model was also validated and compared with other SoTA CNN models, and it outperformed other models.

Deep learning has emerged as a transformative approach in the realm of polyp segmentation within colonoscopy images. In the pursuit of advancing this segmentation approach, this study introduces a novel deep learning architecture that synergistically combines the concept of EfficientNet-B7 and U-Net and propose a new architecture named EU-Net. The EfficientNet-B7, recognized for its scalable and powerful feature extraction capabilities, serves as the encoder of EU-Net. In contrast, the U-Net, renowned for its capability in biomedical image segmentation, functions as the decoder. The prime contribution of the proposed model lies in the analysis of different attention mechanisms before the concatenation of skip connection and the upsampling feature. This attention mechanism enhances the model's focus, ensuring it retains critical regions within images, thereby boosting the accuracy of segmentation tasks. Furthermore, the EU-Net model is enriched with dilated convolutions, enabling it to capture multi-scale context without the loss of resolution, a paramount feature for detecting polyps of varying sizes. The proposed model was rigorously evaluated using the CVC-ClinicDB, a benchmark database for colonoscopy image analysis. The proposed model achieves an IoU of 0.8863. Preliminary results showcase a substantial improvement in segmentation performance, highlighting the potential of the proposed Eu-Net in advancing colorectal cancer prevention efforts.

In the current generation, it has been studied that smoking addiction among the youths is increased exponentially. Since there is a lot of awareness among the people about tobacco use but the youths are exposed to a broader spectrum of the different types of nicotine products like E-cigarettes, or hookah or water pipes, or conventional cigarettes, or dissolvable tobacco, and many more products. Our study aims are to identify some of the demographic factors like age, gender, sex, etc. to predict smoking addiction among the youths of our society. To predict e-cigarette addiction among youths, we considered the wings of Artificial Intelligence (AI) like Machine Learning (ML), and Deep Learning (DL) for better understanding and finding the relationship among the various features. During the data preprocessing we used the elastic net regression technique for feature selection and K-Nearest neighbor for making the accurate predictions. The Hybrid Prediction model was built by the Elastic net regression technique and KNN. It is observed that Elastic net finds a better selection of significant features. The outcome of the suggested pipeline provides high performance on the selection of significant features for the prediction model and provides better accuracy.