Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in the identification and detection of diseases. In this research, we explore the application of a deep learning approach in the analysis of some medical images. Traditional methods have been restricted due to the coarse and granulated appearance of most of these images. Recently, deep learning techniques have produced promising results in the segmentation of medical images for the diagnosis of diseases. This research experiments on medical images using a robust deep learning architecture based on the Fully Convolutional Network- (FCN-) UNET method for the segmentation of three samples of medical images such as skin lesion, retinal images, and brain Magnetic Resonance Imaging (MRI) images. The proposed method can efficiently identify the ROI on these images to assist in the diagnosis of diseases such as skin cancer, eye defects and diabetes, and brain tumor. This system was evaluated on publicly available databases such as the International Symposium on Biomedical Imaging (ISBI) skin lesion images, retina images, and brain tumor datasets with over 90% accuracy and dice coefficient.

中文翻译：

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用于医学图像分析的深度学习方法

感兴趣区域（ROI）的本地化对于医学图像分析至关重要，以帮助识别和检测疾病。在这项研究中，我们探索了深度学习方法在某些医学图像分析中的应用。由于大多数这些图像的粗糙和颗粒状外观，传统方法受到了限制。近来，深度学习技术在用于诊断疾病的医学图像分割中已产生了令人鼓舞的结果。这项研究使用基于完全卷积网络-（FCN-）UNET方法的强大深度学习体系结构对医学图像进行实验，以对医学图像的三个样本进行分割，例如皮肤病变，视网膜图像和脑磁共振成像（MRI）图片。所提出的方法可以有效地识别这些图像上的ROI，以帮助诊断诸如皮肤癌，眼缺陷和糖尿病以及脑肿瘤的疾病。该系统在公开数据库中进行了评估，例如国际生物医学影像研讨会（ISBI）皮肤病变图像，视网膜图像和脑肿瘤数据集，其准确度和骰子系数均超过90％。