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 order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR) has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN), support vector machines (SVM), etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA) based classifier comity learning (GCCL) method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance.

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.