Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels

Nada Fitrieyatul Hikmah, Ariq Dreiki Hajjanto, Armand Faris A. Surbakti, Nadhira Anindyafitri Prakosa, Talitha Asmaria, Tri Arief Sardjono

Research output: Contribution to journalArticlepeer-review

Abstract

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.

Original languageEnglish
Pages (from-to)3995-4004
Number of pages10
JournalInternational Journal of Electrical and Computer Engineering
Volume14
Issue number4
DOIs
Publication statusPublished - Aug 2024

Keywords

  • Convolutional neural network Feature pyramid network MobileNet Single shot MultiBox detector Tumor

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