Kumar Rakesh, Gupta Meenu
Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India.
Health Inf Sci Syst. 2022 Jun 21;10(1):13. doi: 10.1007/s13755-022-00182-y. eCollection 2022 Dec.
Over the past few decades, health care industries and medical practitioners faced a lot of obstacles to diagnosing medical-related problems due to inadequate technology and availability of equipment. In the present era, computer science technologies such as IoT, Cloud Computing, Artificial Intelligence and its allied techniques, etc. play a crucial role in the identification of medical diseases, especially in the domain of Ophthalmology. Despite this, ophthalmologists have to perform the various disease diagnosis task manually which is time-consuming and the chances of error are also very high because some of the abnormalities of eye diseases possess the same symptoms. Furthermore, multiple autonomous systems also exist to categorize the diseases but their prediction rate does not accomplish state-of-art accuracy. In the proposed approach by implementing the concept of Attention, Transfer Learning with the Deep Convolution Neural Network, the model accomplished an accuracy of 97.79% and 95.6% on the training and testing data respectively. This autonomous model efficiently classifies the various oscular disorders namely Choroidal Neovascularization, Diabetic Macular Edema, Drusen from the Optical Coherence Tomography images. It may provide a realistic solution to the healthcare sector to bring down the ophthalmologist burden in the screening of Diabetic Retinopathy.
在过去几十年里，由于技术不足和设备可用性问题，医疗保健行业和医疗从业者在诊断与医疗相关的问题上面临诸多障碍。在当今时代，物联网、云计算、人工智能及其相关技术等计算机科学技术在识别医疗疾病方面发挥着关键作用，尤其是在眼科领域。尽管如此，眼科医生仍需手动执行各种疾病诊断任务，这既耗时，而且出错几率也非常高，因为一些眼部疾病的异常症状相同。此外，也存在多个用于疾病分类的自主系统，但其预测率并未达到当前的先进准确率。在所提出的方法中，通过实施注意力概念、结合深度卷积神经网络进行迁移学习，该模型在训练数据和测试数据上分别实现了97.79%和95.6%的准确率。这个自主模型能够有效地从光学相干断层扫描图像中对各种眼部疾病进行分类，即脉络膜新生血管、糖尿病性黄斑水肿、玻璃膜疣。它可能为医疗保健行业提供一个切实可行的解决方案，以减轻眼科医生在糖尿病视网膜病变筛查中的负担。
