Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia.
Department of Computer Science, Bahria University, Islamabad, Pakistan.
J Healthc Eng. 2022 Sep 26;2022:7028717. doi: 10.1155/2022/7028717. eCollection 2022.
Medical image recognition plays an essential role in the forecasting and early identification of serious diseases in the field of identification. Medical pictures are essential to a patient's health record since they may be used to control, manage, and treat illnesses. On the other hand, image categorization is a difficult problem in diagnostics. This paper provides an enhanced classifier based on the outstanding Feature Selection oriented Clinical Classifier using the Deep Learning (DL) model, which incorporates preprocessing, extraction of features, and classifying. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The proposed methodology is based on feature extraction with the pretrained EfficientNetB0 model. The optimum features enhanced the classifier performance and raised the precision, recall, F1 score, accuracy, and detection of medical pictures to improve the effectiveness of the DL classifier. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The optimum features enhanced the classifier performance and raised the result parameters for detecting medical pictures to improve the effectiveness of the DL classifier. Experiment findings reveal that our presented approach outperforms and achieves 98% accuracy.
医学图像识别在识别领域的重大疾病预测和早期识别中起着至关重要的作用。医学图像是患者健康记录的重要组成部分，因为它们可用于控制、管理和治疗疾病。另一方面，图像分类是诊断中的一个难题。本文提出了一种基于深度学习（DL）模型的优秀特征选择导向临床分类器的增强分类器，该分类器结合了预处理、特征提取和分类。本文旨在为成功的医学成像分类开发最佳的特征提取模型。所提出的方法基于使用预训练的 EfficientNetB0 模型进行特征提取。最优特征增强了分类器的性能，并提高了医学图像的准确率、召回率、F1 分数、准确性和检测率，以提高 DL 分类器的有效性。本文旨在为成功的医学成像分类开发最佳的特征提取模型。最优特征增强了分类器的性能，并提高了医学图像的检测参数，以提高 DL 分类器的有效性。实验结果表明，我们提出的方法表现出色，准确率达到 98%。
