The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.

Child maltreatment is a global issue deeply rooted in cultural, economic, and social practices. The present study investigates the representation of child abuse in the mainstream newspapers of Pakistan (i.e., Dawn and The News) from 2013 to 2020. This specific time frame is that these cases increased during these years. The data regarding the child abuse cases were accessed from the LexisNexis database. A computational technique of topic modeling (Latent Dirichlet Allocations) was applied to discover the hidden topics from the filtered data. The study underpinned Computational Grounded Theory as a theoretical frame for this study. The results reveal topics related to awareness of child abuse, the types of child abuse, and the law and procedural matters specific to child abuse cases in Pakistan. The study brings possible implications that child protection laws are to be improved to overcome the menace of child abuse in Pakistan.