Malaria Cell Classification Using Transfer Learning
Shreyas Deore, Pranav Desai, Ashish Bhosale, Yeetish Bhat, Prof. Saumya Salian · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: In recent years, machine learning techniques, particularly transfer learning, have shown promise in automating malaria cell classification using digital images of blood smears. Transfer learning involves leveraging pre-trained neural networks to extract relevant features from large datasets and applying them to smaller, specialized datasets for improved performance. This abstract provides an overview of the concept of malaria cell classification using transfer learning. It highlights the advantages of using transfer learning, including reduced training time and improved classification accuracy, while addressing challenges such as dataset bias and model interpretability. Further research and development in this area could potentially contribute to the automation and scalability of malaria diagnosis, particularly in resource-limited settings. This study describes a method for classifying malaria cells using convolutional neural networks. It has been evaluated using a standard dataset obtained from the National Library of Medicine, which consists of 27,000microscopic pictures. The accuracy of the neural network has been improved while the total loss has been reduced using the Adam optimizer. Overfitting has been avoided by using dropout regression