Khaliq Ayesha, Ahmad Fahad, Rehman Habib Ur, Alanazi Saad Awadh, Haleem Hamza, Junaid Kashaf, Andrikopoulou Elisavet
Center of Data Science, Government College University Faisalabad, Kotwali Road, Faisalabad, Punjab 37300, Pakistan.
School of Computing, Faculty of Technology, University of Portsmouth, Winston Churchill Ave, Southsea, Portsmouth PO1 3HE, United Kingdom.
Comput Biol Chem. 2025 Jul 4;119:108586. doi: 10.1016/j.compbiolchem.2025.108586.
The integration of artificial intelligence in medical image classification has significantly advanced disease detection. However, traditional deep learning models face persistent challenges, including poor generalizability, high false-positive rates, and difficulties in distinguishing overlapping anatomical features, limiting their clinical utility. To address these limitations, this study proposes a hybrid framework combining Vision Transformers (ViT) and Perceiver IO, designed to enhance multi-disease classification accuracy. Vision Transformers leverage self-attention mechanisms to capture global dependencies in medical images, while Perceiver IO optimizes feature extraction for computational efficiency and precision. The framework is evaluated across three critical clinical domains: neurological disorders, including Stroke (tested on the Brain Stroke Prediction CT Scan Image Dataset) and Alzheimer's (analyzed via the Best Alzheimer MRI Dataset); skin diseases, covering Tinea (trained on the Skin Diseases Dataset) and Melanoma (augmented with dermoscopic images from the HAM10000/HAM10k dataset); and lung diseases, focusing on Lung Cancer (using the Lung Cancer Image Dataset) and Pneumonia (evaluated with the Pneumonia Dataset containing bacterial, viral, and normal X-ray cases). For neurological disorders, the model achieved 0.99 accuracy, 0.99 precision, 1.00 recall, 0.99 F1-score, demonstrating robust detection of structural brain abnormalities. In skin disease classification, it attained 0.95 accuracy, 0.93 precision, 0.97 recall, 0.95 F1-score, highlighting its ability to differentiate fine-grained textural patterns in lesions. For lung diseases, the framework achieved 0.98 accuracy, 0.97 precision, 1.00 recall, 0.98 F1-score, confirming its efficacy in identifying respiratory conditions. To bridge research and clinical practice, an AI-powered chatbot was developed for real-time analysis, enabling users to upload MRI, X-ray, or skin images for automated diagnosis with confidence scores and interpretable insights. This work represents the first application of ViT and Perceiver IO for these disease categories, outperforming conventional architectures in accuracy, computational efficiency, and clinical interpretability. The framework holds significant potential for early disease detection in healthcare settings, reducing diagnostic errors, and improving treatment outcomes for clinicians, radiologists, and patients. By addressing critical limitations of traditional models, such as overlapping feature confusion and false positives, this research advances the deployment of reliable AI tools in neurology, dermatology, and pulmonology.
人工智能在医学图像分类中的整合显著推进了疾病检测。然而，传统深度学习模型面临持续挑战，包括泛化性差、假阳性率高以及区分重叠解剖特征困难，限制了它们的临床应用。为解决这些局限性，本研究提出了一种结合视觉Transformer（ViT）和感知器IO的混合框架，旨在提高多疾病分类准确率。视觉Transformer利用自注意力机制捕捉医学图像中的全局依赖性，而感知器IO优化特征提取以提高计算效率和精度。该框架在三个关键临床领域进行了评估：神经系统疾病，包括中风（在脑中风预测CT扫描图像数据集上测试）和阿尔茨海默病（通过最佳阿尔茨海默病MRI数据集分析）；皮肤疾病，涵盖癣（在皮肤病数据集上训练）和黑色素瘤（用来自HAM10000/HAM10k数据集的皮肤镜图像增强）；以及肺部疾病，重点是肺癌（使用肺癌图像数据集）和肺炎（用包含细菌、病毒和正常X射线病例的肺炎数据集评估）。对于神经系统疾病，该模型的准确率达到0.99，精确率达到0.99，召回率达到1.00，F1分数达到0.99，表明对脑部结构异常有强大的检测能力。在皮肤疾病分类中，其准确率达到0.95，精确率达到0.93，召回率达到0.97，F1分数达到0.95，突出了其区分病变中细粒度纹理模式的能力。对于肺部疾病，该框架的准确率达到0.98，精确率达到0.97，召回率达到1.00，F1分数达到0.98，证实了其在识别呼吸疾病方面的有效性。为了弥合研究与临床实践之间的差距，开发了一个人工智能驱动的聊天机器人用于实时分析，使用户能够上传MRI、X射线或皮肤图像以获得带有置信度分数和可解释见解的自动诊断。这项工作代表了ViT和感知器IO在这些疾病类别中的首次应用，在准确率、计算效率和临床可解释性方面优于传统架构。该框架在医疗环境中早期疾病检测、减少诊断错误以及改善临床医生、放射科医生和患者的治疗结果方面具有巨大潜力。通过解决传统模型的关键局限性，如重叠特征混淆和假阳性，本研究推进了可靠人工智能工具在神经学、皮肤病学和肺病学中的部署。
