Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR's different stages effectively. This paper focuses on classifying the DR's different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer's virtue. Moreover, the stacking of NiN adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better in terms of accuracy, computational resource utilization compared to state-of-the-art methods.

Ensemble models based on deep learning have made significant contributions to the medical field, particularly in the area of disease prediction. Breast cancer is a highly aggressive disease with a high mortality rate. Timely and effective prediction of breast cancer can reduce the risk of it progressing to later stages and the need for unnecessary medications. While previous studies have focused on predicting breast cancer using single-modal datasets, multi-modal datasets that include gene expression (gene exp), clinical, and copy number variation (CNV) data have become available in recent years for predictive model development. However, despite multiple studies using multi-modal data for disease prediction, models designed for breast cancer are typically homogeneous neural networks. This article proposes a heterogeneous deep learning-based ensemble model for effective breast cancer prediction using multi-modal data. The model consists of three phases: feature extraction, stacked feature set creation, and using extracted features as input for a stacked-based model using a random forest algorithm for effective prediction. For feature extraction, convolutional neural networks (CNNs) are used for clinical and gene expression data, and deep neural networks (DNNs) are used for CNV data. The extracted features from CNNs and DNNs are stacked to create a comprehensive feature set. The simulation results demonstrate the superiority of the proposed framework in terms of accuracy compared to uni-modal and homogeneous model-multi-modal frameworks.

Generative Adversarial Networks (GANs) have shown promising prospects and achieved significant results in medical image analysis tasks. This article provides a comprehensive review of recent research on GANs and their variants in medical applications, including tasks such as image synthesis, segmentation, classification, detection, denoising, reconstruction, fusion, registration, and prediction. We summarize and analyze the reviewed literature, with a focus on model framework design,dataset sources, and performance evaluation. Our research findings are presented in the form of tables. In the end,article discusses open challenges and directions for future research.

In the field of medical image analysis, MRI and CT, among other multimodal medical images, play crucial roles. To overcome the limitations of image acquisition, researchers have proposed medical image synthesis techniques, including both traditional methods and deep learning approaches. In this study, we introduce a universal framework based on cycleGAN for generating CT images from MRI data.This framework incorporates a hinge loss function to establish mappings between different modalities and enhance structural consistency between input and output images. We also employ a switchable normalization technique to improve model stability and reduce manual intervention. These enhancements result in the generation of higher-quality synthetic images while avoiding gradient issues and mode collapse.The results of this research demonstrate significant progress in medical image synthesis. Compared to existing methods, our model exhibits superior performance in quantitative evaluation metrics while maintaining better diversity and structural consistency. This indicates that our framework holds promise in medical image synthesis and can provide valuable support in areas such as disease prediction and treatment.

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.