Publications by authors named "Mohammed Alojail"
Article Synopsis
- Medical imaging is essential for disease diagnosis, but traditional methods face challenges like subjective interpretation and difficulty with complex images.
- This research introduces an integrated deep learning model using pre-trained networks (VGG16, ResNet50, InceptionV3) aimed at improving lung cancer detection accuracy by streamlining image data and enhancing feature extraction.
- The model demonstrated impressive performance, achieving 98.18% accuracy and strong precision and recall rates during validation on a specific lung cancer dataset from Iraq, showcasing the promise of advanced deep learning approaches in medical diagnostics.
Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed.
View Article and Find Full Text PDFHandwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and radiology reports, should be labelled with specific information such as disease type, features, and anatomical location for more effective use. The semantic annotation of vast collections of biological and biomedical texts, like scientific papers, medical reports, and general practitioner observations, has lately been examined by doctors and scientists.
View Article and Find Full Text PDF