Accurate and efficient segmentation of gastro-intestinal (GI) tract images play a pivotal role in medical diagnosis. However, conventional deep learning models, such as U-Net, often incur high computational costs and memory overheads, hindering their deployment in resource-constrained clinical settings. This article proposes a novel hierarchical attention lightweight U-Net (HALU-Net) architecture designed specifically to address these limitations. HALU-Net incorporates a computationally efficient U-Net backbone, optimized through techniques like depth-wise separable convolutions and a hierarchical attention mechanism. While depth-wise separable convolution helps to reduce the model parameters, the hierarchical attention enables the model to selectively focus on both localized, fine-grained details and global contextual information within GI images at every decoding stage. This multi-level attention approach is essential for accurate segmentation of complex and variable GI tract structures. The HALU-Net model is rigorously evaluated on the publicly available UW-Madison GI tract image segmentation dataset. Results demonstrate that HALU-Net achieves competitive segmentation performance as measured by metrics such as dice coefficient and Jaccard index while boasting a substantially reduced parameter count compared to prevailing U-Net architectures. This significant decrease in computational complexity and memory requirements positions HALU-Net as a promising solution for facilitating accurate medical diagnoses of GI conditions, even in computation-constrained environments.

Breast cancer is a prevalent and life-threatening disease where early and accurate diagnosis is critical for effective treatment. Conventional histopathological analysis, while the standard for diagnosis, can be laborious and is subject to inter-observer variability, highlighting the need for robust automated methods. This article introduces EVC-Net, a novel hybrid deep learning framework designed to automate the classification of breast cancer from histopathological images. EVC-Net synergistically integrates an EfficientNetV2S for fine-grained texture feature extraction, a vision transformer (ViT) for capturing global context, and a capsule network to preserve spatial hierarchies within tissue structures. The proposed model is evaluated on the public BreakHis dataset. Across all four magnification levels, EVC-Net demonstrates robust performance, achieving an average accuracy of 0.985 and an AUC-ROC of 0.994 for binary (benign vs. malignant) classification. For the eight-subtype multi-class task, the model maintains high efficacy, attaining an average accuracy of 0.954 and an AUC-ROC of 0.980. Furthermore, interpretability analysis using Grad-CAM is conducted, generating heatmaps overlay visualization to understand the rational behind model’s predictions. These results demonstrate the potential of the EVC-Net framework to enhance diagnostic accuracy and consistency, offering valuable support for clinical workflows in oncology.

Data intensive applications are one of the most critical real-time applications which are desired in most of the new-normal practices such as recommendation systems, social media analytics systems, fake news detection systems, etc. However, to deploy such solutions for real-time usage, software testing and quality assurance plays a vital role to understand the application behavior. The characteristic 4 Vs of big data adds complexities or challenges that need to be addressed for real-time applications or development. Testing of big data applications can be made efficient by designing and executing test plans; approach and strategy for all V’s of big data. This tutorial comprehensively covers both the theoretical and practical aspects of testing data-intensive applications. The tutorial discusses testing data-intensive applications built on top of modern big data frameworks such as Hadoop, Spark, Flink, NoSQL, Hive, Zookeeper, Elastic Search, Flume, and Kafka. The hands-on with MapReduce unit testing, Spark streaming testing, Kafka unit testing and related testing libraries has been covered with simple integration of testing examples and test case driven developments.

Adversarial attacks against deep learning models have gained significant attention and recent works have pro-posed explanations for the existence of adversarial examples and techniques to defend the models against these attacks. Attention in computer vision has been used to incorporate focused learning of important features and has led to improved accuracy. Recently, models with attention mechanisms have been proposed to enhance adversarial robustness. Following this context, this work aims at a general understanding of the impact of attention on adversarial robustness. This work presents a comparative study of adversarial robustness of non-attention and attention based image classification models trained on CIFAR-10, CIFAR-100 and Fashion MNIST datasets under the popular white box and black box attacks. The experimental results show that the robustness of attention based models may be dependent on the datasets used i.e. the number of classes involved in the classification. In contrast to the datasets with less number of classes, attention based models are observed to show better robustness towards classification.

Autonomous detection and classification of objects are admired area of research in many industrial applications. Though, humans can distinguish objects with high multi-granular similarities very easily; but for the machines, it is a very challenging task. The convolution neural networks (CNN) have illustrated efficient performance in multi-level representations of objects for classification. Conventionally, the existing deep learning models utilize the transformed features generated by the rearmost layer for training and testing. However, it is evident that this does not work well with multi-granular data, especially, in presence of deceptive similar classes (almost similar but different classes). The objective of the present research is to address the challenge of classification of deceptively similar multi-granular objects with an ensemble approach that utilizes activations from multiple layers of CNN (deep features). These multi-layer activations are further utilized to build multiple deep decision trees (known as Random forest) for classification of objects with similar appearance. The Fruits-360 dataset is utilized for evaluation of the proposed approach. With extensive trials it was observed that the proposed model outperformed over the conventional deep learning approaches.

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.

With the increasing applications of deep learning in biomedical image analysis, in this article we introduce an inception U-Net architecture for automating nuclei detection in microscopy cell images of varying size and modality to help unlock faster cures, inspired from Kaggle Data Science Bowl Challenge 2018 (KDSB18). This study follows from the fact that most of the analysis requires nuclei detection as the starting phase for getting an insight into the underlying biological process and further diagnosis. The proposed architecture consists of a switch normalization layer, convolution layers, and inception layers (concatenated 1x1, 3x3, and 5x5 convolution and the hybrid of a max and Hartley spectral pooling layer) connected in the U-Net fashion for generating the image masks. This article also illustrates the model perception of image masks using activation maximization and filter map visualization techniques. A novel objective function segmentation loss is proposed based on the binary cross entropy, dice coefficient, and intersection over union loss functions. The intersection over union score, loss value, and pixel accuracy metrics evaluate the model over the KDSB18 dataset. The proposed inception U-Net architecture exhibits quite significant results as compared to the original U-Net and recent U-Net++ architecture.