Browsing by Author "Almenwer, Sahar"

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Early Detection of Pleuropulmonary Blastoma Using Transformers Models
(Bowie State University, 2024) Almenwer, Sahar; El-Sayed, Hoda
Childhood cancer is the second leading cause of death among children under the age of fifteen, according to the American Cancer Society. The number of diagnosed cancer cases in children continues to rise each year, leading to many tragic fatalities. One specific type of cancer, pleuropulmonary blastoma (PPB), affects children from newborns to those as old as six years. The most common way to diagnose PPB is through imaging; this method is quick, cost-effective, and does not require specialized equipment or laboratory tests. However, relying solely on imaging for early detection of PPB can be challenging because of lower accuracy and sensitivity. It is time consuming and susceptible to errors because of the numerous potential differential diagnoses. A more accurate diagnosis of PPB depends on identifying mutations in the DICER1 gene. Recent advancements in biological analysis and computer learning are transforming cancer treatment. Deep learning (DL) methods for diagnosing PPB are becoming increasingly popular. Despite facing some challenges, DL shows a significant promise in supporting oncologists. However, some advanced models possess a limited local receptive field, which may restrict their ability to comprehend the overall context. This research employs the vision transformer (ViT) model to address these limitations. ViT reduces computation time and yields better results than existing models. It utilizes self-attention among image patches to process visual information effectively. The experiments in this study are conducted using two types of datasets, medical images and genomic datasets, employing two different methodologies. One approach uses the ViT model combined with an explainability framework on large medical image datasets with various modalities. The other involves developing a new hybrid model that integrates the vision transformer with bidirectional long short-term memory (ViT-BiLSTM) for genomic datasets. The results demonstrate that the ViT model and the new hybrid model, ViT-BiLSTM, significantly outperform established models, as validated by multiple performance metrics. Consequently, this research holds great promise for the early diagnosis of PPB, reducing misdiagnosis occurrences, and facilitating timely intervention and treatment. These findings could revolutionize medical diagnosis and shape the future of healthcare.
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