I-Brainer: Artificial intelligence/Internet of Things (AI/IoT)-Powered Detection of Brain Cancer.

IF 1.1 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Current Medical Imaging Reviews Pub Date : 2025-02-04 DOI:10.2174/0115734056333393250117164020

Abdullahi Umar Ibrahim, Ikedichukwu Onyemaucheya Nwaneri, Mercel Vubangsi, Fadi Al-Turjman

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引用次数: 0

Abstract

Background/objective: Brain tumour is characterized by its aggressive nature and low survival rate and thus regarded as one of the deadliest diseases. Thus, miss-diagnosis or miss-classification of brain tumour can lead to miss treatment or incorrect treatment and reduce survival chances. Therefore, there is need to develop a technique that can identify and detect brain tumour at early stages.

Methods: Here, we proposed a framework titled I-Brainer which is an Artificial Intelligence/Internet of Things (AI/IoT)-powered classification of MRI. We employed a Br35H+SARTAJ brain MRI dataset which contain 7023 total images which include No tumour, pituitary, meningioma and glioma. In order to accurately classified MRI into 4-class, we developed LeNet model from scratch, implemented 2 pretrained models which include EfficientNet and ResNet-50 as well feature extraction of these models coupled with 2 Machine Learning classifiers k-Nearest Neighbours (KNN) and Support Vector Machines (SVM).

Result: Evaluation and comparison of the performance of 3 models has shown that EfficientNet+SVM achieved the best result in terms of AUC (99%) and ResNet-50-KNN ranked higher in terms of accuracy (94%) on testing dataset.

Conclusion: This framework can be harness by patients residing in remote areas and as confirmatory approach for medical experts.

查看原文本刊更多论文

I-Brainer：人工智能/物联网（AI/IoT）驱动的脑癌检测。

背景/目的：脑肿瘤具有侵袭性和低存活率的特点，被认为是最致命的疾病之一。因此，脑肿瘤的漏诊或漏分可能导致漏诊或错误治疗，降低生存机会。因此，有必要开发一种能够在早期阶段识别和检测脑肿瘤的技术。方法：在这里，我们提出了一个名为I-Brainer的框架，这是一个人工智能/物联网（AI/IoT）驱动的MRI分类。我们采用Br35H+SARTAJ脑MRI数据集，共包含7023张图像，包括无肿瘤、垂体、脑膜瘤和胶质瘤。为了准确地将MRI分类为4类，我们从零开始开发LeNet模型，实现了包括EfficientNet和ResNet-50在内的2个预训练模型，并结合2个机器学习分类器k-Nearest neighbors （KNN）和支持向量机（SVM）对这些模型进行特征提取。结果：对3种模型的性能进行评估和比较，在测试数据集上，effentnet +SVM在AUC方面取得了最好的结果（99%），ResNet-50-KNN在准确率方面排名更高（94%）。结论：该框架可为偏远地区患者使用，并可作为医学专家的验证方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current Medical Imaging Reviews 医学-核医学

CiteScore

2.60

自引率

0.00%

发文量

246

审稿时长

1 months

期刊介绍： Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.