  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.
