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All Journal Journal of Applied Engineering and Technological Science (JAETS)
Hanan H. Al-Nidawi
Ministry of Education, General Directorate of Administrative Affairs, Baghdad, Iraq
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Human Brain Tumors Detected by A Deep Learning Method Through a Pre-Trained Model Hanan H. Al-Nidawi; Farah AL-Jibory; Mohammed S. Hamid; Ruaa S. Salman
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9383

Abstract

As a result, abnormal cells develop in the body, leading to a highly constitutive cell type that is a significant risk to the patient's functional capabilities and vital processes. The early and accurate recognition of such cells is crucial for accurate diagnosis and prognosis, and this recognition is made possible by medical imaging techniques, particularly magnetic resonance imaging (MRI). Despite advances in 3D learning models, several scientific studies involving deep convolutional networks (CNNs) still face numerous challenges. These challenges include the underutilization of spatial information, the inability of traditional data reduction techniques to minimise data dimensionality during the assembly phase, and suboptimal data processing during the data synchronisation or listening. In addition, some approaches require large volumes of data to achieve sufficient performance, which limits their applicability to real-world healthcare scenarios. This paper discusses the V-Net model that has been trained for a relatively long time to process volumetric 3D data, including a wide variety of very small sub-3D spatial volumes. This work used a large global MRI dataset, split into 80% for the training set and 20% for the test set. Before the tests, the images were preprocessed by resizing them to 128 × 128, applying Min-Max normalisation, and CLAHE (Contrast Limited Adaptive Histogram Equalisation) to enhance contrastof the images. The results showed that the proposed model achieved a 99% improvement in tumour detection performance over all other approaches. The findings indicate that employing specialised architectures like V-Net may significantly enhance the efficiency of medical diagnostic imaging specialists.
Co-Authors Farah AL-Jibory Mohammed S. Hamid Ruaa S. Salman