Trisheela S, Fernandes Roshan, Rodrigues Anisha P, Supreeth S, Ambika B J, Pareek Piyush Kumar, Godi Rakesh Kumar, Shruthi G
Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India.
Department of Cyber Security, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, India.
Int J Biomed Imaging. 2025 May 30;2025:5535505. doi: 10.1155/ijbi/5535505. eCollection 2025.
The objective of AI research and development is to create intelligent systems capable of performing tasks and reasoning like humans. Artificial intelligence extends beyond pattern recognition, planning, and problem-solving, particularly in the realm of machine learning, where deep learning frameworks play a pivotal role. This study focuses on enhancing brain tumour detection in MRI scans using deep learning techniques. Malignant brain tumours result from abnormal cell growth, leading to severe neurological complications and high mortality rates. Early diagnosis is essential for effective treatment, and our research aims to improve detection accuracy through advanced AI methodologies. We propose a modified DarkNet-53 architecture, optimized with invasive weed optimization (IWO), to extract critical features from preprocessed MRI images. The model's presentation is assessed using accuracy, recall, loss, and AUC, achieving a 95% success rate on a dataset of 3264 MRI scans. The results demonstrate that our approach surpasses existing methods in accurately identifying a wide range of brain tumours at an early stage, contributing to improved diagnostic precision and patient outcomes.
人工智能研发的目标是创建能够像人类一样执行任务和进行推理的智能系统。人工智能不仅仅局限于模式识别、规划和问题解决，特别是在机器学习领域，深度学习框架在其中发挥着关键作用。本研究聚焦于使用深度学习技术增强磁共振成像（MRI）扫描中的脑肿瘤检测。恶性脑肿瘤由异常细胞生长导致，会引发严重的神经并发症和高死亡率。早期诊断对于有效治疗至关重要，我们的研究旨在通过先进的人工智能方法提高检测准确性。我们提出一种经入侵杂草优化（IWO）优化的改进型DarkNet-53架构，用于从预处理的MRI图像中提取关键特征。使用准确率、召回率、损失和曲线下面积（AUC）评估该模型的表现，在一个包含3264次MRI扫描的数据集上实现了95%的成功率。结果表明，我们的方法在早期准确识别多种脑肿瘤方面超越了现有方法，有助于提高诊断精度和改善患者预后。
