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Paper Title :
"Utilizing Deep Learning for Malaria Cell Identification: A Comparative Analysis of Alex Net and Dense Net"

Author :Chandradhitiya T V, Kavin Kumar, Barath M R, Jagadeesh P

Article Citation :Chandradhitiya T V ,Kavin Kumar ,Barath M R ,Jagadeesh P , (2023 ) " "Utilizing Deep Learning for Malaria Cell Identification: A Comparative Analysis of Alex Net and Dense Net" " , International Journal of Soft Computing And Artificial Intelligence (IJSCAI) , pp. 29-35, Volume-11,Issue-2

Abstract : Malaria is an acute febrile illness caused by Plasmodium parasites, which are being spread to people through the bites of infected female Anopheles mosquitoes. The aim of this project is to identify whether a cell is malaria infected or not by applying machine learning (Deep learning) algorithms that is Alex net and Dense net. The dataset which is used for reference consists of totally 27,558 images out of which 13,780 images are infected and rest are uninfected cells and is taken from the NIH Website. In the project, the sample of that dataset is taken and algorithms are applied in order to evaluate the dataset. The machine is trained to classify and detect if the cell is parasitized or uninfected. An in breadth and depth analysis of various features classifiers like Alexnet and Densenet, and compare their performance by tuning different hyperparameters.DenseNet is a deep convolutional neural network architecture developed for image classification tasks. It is characterized by densely connected layers, which enables better feature reuse and gradient flow throughout the network. AlexNet is a deep neural network that can learn complex features from images, and it has been shown to be very effective at image classification tasks. The output is combined using ensemble technique and the performance of these classifiers is evaluated. Subtitle: Harnessing the Power of Artificial Intelligence to Combat a Global Health Challenge. Keywords - Traffic prediction, Machine learning, Dynamic optical networks, Service chains, Recurrent neural networks, Convolutional neural networks, Resource allocation, Quality of Service (QoS), Network optimization.

Type : Research paper

Published : Volume-11,Issue-2


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