The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due to changes in certain supplements, medical conditions, obesity, and diet, which causes kidney function and ultimately leads to complications such as chronic kidney disease, kidney failure, and other renal disorders. Combining patient metadata with computed tomography (CT) images is essential to accurately and timely diagnosing such complications. Deep Neural Networks (DNNs) have transformed medical fields by providing high accuracy in complex tasks. However, the high computational cost of these models is a significant challenge, particularly in real-time applications. This paper proposed SpinalZFNet, a hybrid deep learning approach that integrates the architectural strengths of Spinal Network (SpinalNet) with the feature extraction capabilities of Zeiler and Fergus Network (ZFNet) to classify kidney disease accurately using CT images. This unique combination enhanced feature analysis, significantly improving classification accuracy while reducing the computational overhead. At first, the acquired CT images are pre-processed using a median filter, and the pre-processed image is segmented using Efficient Neural Network (ENet). Later, the images are augmented, and different features are extracted from the augmented CT images. The extracted features finally classify the kidney disease into normal, tumor, cyst, and stone using the proposed SpinalZFNet model. The SpinalZFNet outperformed other models, with 99.9% sensitivity, 99.5% specificity, precision 99.6%, 99.8% accuracy, and 99.7% F1-Score in classifying kidney disease. Graphical Abstract

The early detection of breast cancer plays a critical role in improving survival rates and facilitating precise medical interventions. Therefore, the automated identification of breast abnormalities becomes paramount, significantly enhancing the prospects of successful treatment outcomes. To address this imperative, our research leverages multiple modalities such as MRI, CT, and mammography to detect and screen for breast cancer. Our proposed methodology integrates innovative modules such as the Distributed Load-based Dilated Convolution (DLM) and Multi-Head Dense Dilated Convolution (MHD) modules. These modules are designed to capture multi-scale information crucial for accurate tumor detection while simultaneously minimizing the parameter count, thus enhancing computational efficiency. Additionally, an Optimal Multi-Level Attention (OMLA) and Key Aspect Attention (KA) modules are adept at aggregating task-relevant information effectively, contributing to improved model performance and accuracy. Our experimental evaluation utilizing multimodal clinical data underscores the effectiveness of the proposed method, achieving an impressive Dice coefficient of 86.756%, 96.931%, and 97.813% for segmentation and classification accuracy of 89.982%, 97.028%, and 98.760% for Mammogram, CT, and MRI datasets, respectively. These results validate the efficacy and potential clinical utility of our approach in enhancing breast cancer detection and diagnosis. We introduce a novel framework for the classification and segmentation of multimodal medical images acquired by a variety of modalities for breast cancer detection and identification. In addition to improving segmentation accuracy, our proposed Computer-Aided Diagnosis (CAD) method utilizes the segmentation mask as an additional channel feature to enhance the classification outcomes of breast tumors. Additionally, we have meticulously curated a comprehensive CT breast cancer dataset, comprising various clinical data types related to breast tumors, including detailed segmentation and classification annotations. This dataset surpasses previous efforts in its depth and breadth, providing a robust foundation for future research endeavors.

The Internet of Things (IoT) has experienced significant growth in recent years, with IoT connections surpassing those of traditional connected devices in the last few years. However, with this growth comes an increased risk of IoT security breaches as cybercriminals take advantage of lax security measures at the endpoint level. One of the main challenges in IoT security is the lack of proper protocols, policies, and procedures to protect these devices from malware and malicious software. This review article focuses on the recent advancements in machine learning-based malware detection techniques for IoT devices. We discuss the challenges of detecting malware in IoT devices, including the limited resources and processing power, as well as the diversity of device types and operating systems. We also review recent machine learning-based malware detection techniques for IoT devices, including deep learning, ensemble learning, and transfer learning, and evaluate their efficiency. This review aims to provide a comprehensive understanding of the current state-of-the-art machine learning-based malware detection techniques for IoT devices, highlighting the potential and limitations of these techniques and the role of analytics in future research directions.