Siti Nurul Aqmariah Mohd Kanafiah^1,*, Mohd Yusoff Mashor¹, Zeehaida Mohamed², Yap Chun Way¹, Shazmin Aniza Abdul Shukor¹, Yessi Jusman³

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 687-697, 2022, DOI:10.32604/iasc.2022.024361

Abstract Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, the microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum… More >

G. Madhu^1,*, A. Govardhan², B. Sunil Srinivas³, Kshira Sagar Sahoo⁴, N. Z. Jhanjhi⁵, K. S. Vardhan¹, B. Rohit⁶

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 903-919, 2021, DOI:10.32604/cmc.2021.016114

Abstract Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully trained capsule networks for… More >