Malaria parasite detection using advanced deep Learning techniques
S Shashikiran, Naidu Srinivas Kiran Babu, P Rama, Sandip Mondal, Syed Sadaqathulla, K S Pramod · 2024
The parasite Plasmodium, which is carried by Anopheles mosquitoes, is what causes the fatal malaria sickness. It produces chills, headaches, exhaustion, and fever. Severe cases also result in mortality and organ failure. Malaria diagnosis is typically made using microscope results. It is necessary to create new instruments and algorithms for the diagnosis and treatment of malaria. A CNN-based technique for feature extraction from thin, raw blood smear images is proposed in this work. An SVM classifier is then used to categorize the collected features into one of the four species types: Falciparum, Vivax, Ovale, and Malariae. The suggested model demonstrates the efficacy of deep learning methods in the diagnosis of malaria and the classification of species with an exceptionally high accuracy. This involves the process of making forecasts by utilizing patterns found in extensive datasets. Particularly in places with limited resources, the deep learning approach, which is advised work, lowers the cost of diagnosis while also offering a more dependable diagnosis, smartphones are used to capture the little blood smear photos, making it rapid and simple to acquire datasets. Additionally, it may send blood smear images fast for an early diagnosis. In the proposed study, a convolutional layer made up of the images is applied using batch normalization and ReLu to define residual units. Ultimately, a completely linked layer comes before it to produce the intended output, which may be pictures free of malaria infection or pictures that show the infected ones, with that also plotting the accuracy and performance indicators of the model which includes graphs and confusionmatrices.