Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a time-consuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community.

Confidentiality and efficient bandwidth utilization require a combination of compression and encryption of digital images. In this paper, a new method for joint image compression and encryption based on set partitioning in hierarchical trees (SPIHT) with optimized Kd-tree and multiple chaotic maps was proposed. First, the lossless compression and encryption of the original images were performed based on integer wavelet transform (IWT) with SPIHT. Wavelet coefficients undergo diffusions and permutations before encoded through SPIHT. Second, maximum confusion, diffusion and compression of the SPIHT output were performed via the modified Kd-tree, wavelet tree and Huffman coding. Finally, the compressed output was further encrypted with varying parameter logistic maps and modified quadratic chaotic maps. The performance of the proposed technique was evaluated through compression ratio (CR) and peak-signal-to-noise ratio (PSNR), key space and histogram analyses. Moreover, this scheme passes several security tests, such as sensitivity, entropy and differential analysis tests. According to the theoretical analysis and experimental results, the proposed method is more secure and decreases the redundant information of the image more than the existing techniques for hybrid compression and encryption.

Intelligent agents are increasingly being deployed in complex, dynamic environments where contextual understanding and adaptive decision-making are critical. This paper proposes a novel framework for intelligent agents that leverage narrative memory a structured, story-based representation of past experiences to enhance their ability to reason, learn, and act in dynamic scenarios. The framework integrates narrative theory with machine learning techniques to enable agents to construct, store, and retrieve narratives that capture temporal, causal, and contextual relationships. We evaluate the framework through a series of simulations and real-world case studies, demonstrating its effectiveness in improving decision-making, adaptability, and user interaction. The results suggest that narrative memory-based agents outperform traditional approaches in tasks requiring long-term context retention, causal reasoning, and explain ability. This research contributes to the advancement of intelligent systems by bridging the gap between computational efficiency and human-like narrative understanding.