An Enhanced Deep Learning Approach for Brain Cancer MRI Images Classification using Residual Networks

Publication date: Available online 10 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Sarah Ali Abdelaziz Ismael, Ammar Mohammed, Hesham HefnyAbstractCancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research

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Publication date: Available online 3 June 2020Source: Journal of the American College of RadiologyAuthor(s): Ruth C. Carlos, Katy Lowry, Gelareh Sadigh
Source: Journal of the American College of Radiology - Category: Radiology Source Type: research
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Source: Spine - Category: Orthopaedics Tags: CLINICAL CASE SERIES Source Type: research
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Source: Spine - Category: Orthopaedics Tags: HEALTH SERVICES RESEARCH Source Type: research
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Source: Spine - Category: Orthopaedics Tags: LITERATURE REVIEW Source Type: research
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Source: Spine - Category: Orthopaedics Tags: SURGERY Source Type: research
Authors: Carkic J, Nikolic N, Nisevic J, Lazarevic M, Kuzmanovic-Pficer J, Jelovac D, Milasin J Abstract Oral carcinoma is the sixth most common malignancy worldwide, with survival rates of approximately 50%. The major type of oral cancer, present in 90% of the cases, is oral squamous cell carcinoma (OSCC). The genetic background predisposing an individual to OSCC is complex and largely unknown. Studies have suggested that endothelial nitric oxide synthase (eNOS) gene polymorphisms modulate the cancer risk, prompting us to assess the impact of three functional eNOS gene polymorphisms on OSCC risk. The present study...
Source: Journal of oral science - Category: Dentistry Tags: J Oral Sci Source Type: research
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Source: Current Opinion in Clinical Nutrition and Metabolic Care - Category: Nutrition Tags: GENES AND CELL METABOLISM: Edited by Anastasia Z. Kalea and Dorothy Klimis-Zacas Source Type: research
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Source: Journal of Clinical Gastroenterology - Category: Gastroenterology Tags: ALIMENTARY TRACT: Original Articles Source Type: research
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Source: Frontiers in Oncology - Category: Cancer & Oncology Source Type: research
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Source: Indian Journal of Cancer - Category: Cancer & Oncology Authors: Source Type: research
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