Pathology deals with the practice of discovering the reasons for disease by analyzing the body samples. The most used way in this field, is to use histology which is basically studying and viewing microscopic structures of cell and tissues. The slide viewing method is widely being used and converted into digital form to produce high resolution images. This enabled the area of deep learning and machine learning to deep dive into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the blood images with an accuracy of 95.24%. The performance of proposed model is better than existing standard architectures and work done by various researchers. Thus model will enable development of pathological system which will reduce human errors and daily load on laboratory men. This will in turn help pathologists in carrying out their work more efficiently and effectively.
翻译:病理学涉及通过分析身体样本来发现疾病原因的做法。 该领域最常用的方法是使用基本研究和观察细胞和组织微小结构的病理学。 幻灯片查看方法被广泛使用并转换为数字形式, 以产生高分辨率图像。 这使深层学习和机器学习领域能够深入潜入医学领域。 在这次研究中, 提出了一个基于神经的网络, 将血细胞图像分类为不同类别。 当输入图像通过拟议结构传递时, 并且所有超强参数和辍学率值都按照拟议算法使用时, 然后模型将血液图像分类, 精确度为95. 24%。 所提议模型的性能优于现有标准结构和各种研究人员所做的工作。 这样模型将使得病理系统的发展能够减少人类的错误和实验室人员每天的负担。 这反过来将有助于病理学家更高效地开展工作。