This tutorial provides a step-by-step easy guide on how to implement and train a CNN model for Malaria cell classification using TensorFlow and Keras. What You’ll Learn : Data Preparation — In this part, you’ll download the dataset and prepare the data for training. This involves tasks like preparing the data , splitting into training and testing sets, and data augmentation if necessary. CNN Model Building and Training — In part two, you’ll focus on building a Convolutional Neural Network (CNN) model for the binary classification of malaria cells. This includes model customization, defining layers, and training the model using the prepared data. Model Testing and Prediction — The final part involves testing the trained model using a fresh image that it has never seen before. You’ll load the saved model and use it to make predictions on this new image to determine whether it’s infected or not. You can find link for the code in the blog : https://eranfeit.net/how-to-classify-malaria-cells-using-convolutional-neural-network/ Full code description for Medium users : https://medium.com/@feitgemel/how-t...ing-convolutional-neural-network-c00859bc6b46 You can find more tutorials, and join my newsletter here : https://eranfeit.net/ Check out our tutorial here : Enjoy Eran #Python #Cnn #TensorFlow #deeplearning #neuralnetworks #imageclassification #convolutionalneuralnetworks #computervision #transferlearning
