Capsule Networks for Malaria Parasite Classification: An Application Oriented Model
G. Madhu, Aliseri Govardhan, B. Srinivas, Shilhora Akshay Patel, Rohit Boddeda, Lalith Bharadwaj Baru · 2020 IEEE International Conference for Innovation in Technology (INOCON) · 2020
The epidemic of malaria is a death-dealing infectious disease caused by mosquito spreading across the world. In this technological era, automated diagnosis is worthwhile for accurate and faster solutions. In this work, a novel application-oriented diagnostic model was deployed to detect and classify thin-blood smear images infected with malaria. The Features from thin-blood smears are extracted using a series of Convolution-Neural-Networks and classified with novel Capsule-Networks by understanding the spatial relationship of thin-blood films. The experimentation is compared to VGG-16, ResNet-50, DenseNet-121 architectures with immense depth varying from 16 to 121 layers. The proposed Capsule-Network is 8 layer deep and tends to outperform attaining classification accuracy of 96.9% and specificity, sensitivity scores were 94.95%, 98.18%. This novel model was deployed as a web-application to act as an aid for such a havoc problem and to transfer applicability to every user in need by classifying images in less than 3 seconds.