Inception Model for Malaria Detection Using Malaria Cell Images Dataset

In this study, deep learning models were used to perform binary classification of malaria-infected and uninfected human skin cells. The study compares the efficacy of four different models, namely Convolutional Neural Network (CNN), Inception, ResNet, and VGG19, in detecting malaria. The findings through experimentation and analysis show that the Inception model beats its competitors, obtaining the greatest accuracy rate of 94.5%• This high precision score demonstrates the efficacy of Inception's novel design, which includes inception modules for simultaneous multi-scale feature extraction. While CNN, ResNet, and VGG19 all perform admirably, the Inception model's greater accuracy emphasizes its potential as a robust method for the automated detection of malaria-infected cells in microscopic images. It helps in biomedical. it also helps to improve quality of life.