Thanks for your kind feedback.

Keep it up

I appreciate that!

can you please tell me how to predict in 1 class of image?

My pleasure!

You're welcome 😊

1. Please run one line at a time and see the parameter type in the variable explorer, you'll find the answer. In summary, y has already been converted to a numpy array during train and test data separation step (using to_categorical). Therefore, we convert X only to numpy array as it is a list and not an array. 2. Normalization is not a necessity as I am using relu activation which can deal with real value numbers. Also, I am using batch normalization in the hidden layers in my model, so all numbers will be normalized before going to the next layer. May be I should record a video on parameters and normalization. Thanks for asking questions.

As far as I remember there are no tricks with the dataset, just a regular compressed folder. May be your winzip has issues. I use winrar for these type of tasks, please give that a try.

@@DigitalSreeni thank you ..will give that a try.

This tutorial explains classification. If you want segmentation you may want to look at U-net for semantic segmentation.

@@DigitalSreeni I want to segment infected red blood cells only from malaria blood smear images. Please suggest me, semantic U-net technique will work or need to apply any other technique?

@@shankaraggarwal4234 start watching from video 71/72, a very good example of cell segmentation using U-Net was made

Please checkout my videos on the topic of multiclass classification.

@@DigitalSreeni Thank you!

The entire dataset has about 13K images for parasitized and another 13K for uninfected. My system does not have the type of resources required to process all images. Therefore, I took a subset of those images (about 500 each) for the tutorial. I dumped them in a folder called cell_images2. Sorry for any confusion. After resizing the images are not saved locally, the information is captured in an array called 'dataset'. This dataset has been divided into training and testing sets for training the network. Of course, the train part of the data was used for training. Based on the code it appears that I did not use the test part for validation but instead I further split train to 90% for training and 10% for validation. Please go through the code carefully as it forms the basis for most deep learning approaches.

@@DigitalSreeni Thanks a lot. I thought images will be saved locally. That's why I was confused. Now it's clear.

It works fine for me. Please watch my video 147 where I just copied the spyder code and executed on colab, didn’t change a thing. It works with CPU and GPU. Please check your code.

I am not sure I understand your point. If you are referring to segmenting images then I should mention that these type of images can be challenging to segment. They also come in various shades of colors which means you first have to find a way to normalize images and then segment them. Also, segment them for what? And who is going to label them manually? In summary, classification problems do not need the complexity of pixel segmentation.

Have you fixed this? I'm getting the same first error

Classification of Hela cells into what? Do you want to classify entire cell or organelles in cells?

@@DigitalSreeni entire cell..classify images of Hela cells into a dead cell or live cell

Yes sir please upload multiclass classification from images.

Neural networks are stochastic; they use randomness as part of the algorithms. This means you will get slightly different result each time but on average your results should be close enough if the training converges. You can try to minimize the randomness by fixing the random number seed. All random numbers use a random seed to produce a random number; if you fix this seed you'll generate same 'randomness' each time. I siggest placing these 4 lines at the top of your code. from numpy.random import seed seed(20) #Can be any number but use the same number to repeat experiments. from tensorflow import set_random_seed set_random_seed(42) #Can be any number but use the same number to repeat experiments. Remember that this only controls part of your randomness but there are other sources for randomness.

To predict, you need to load the model and apply on your other images, very similar to how you apply to test images. My video number 131 will give information on how to load a model.

Just use model.predict(img). Of course, you need to preprocess your images just the way you did your training data. Please watch my recent videos (Videos 128 and later) for more information. Here is some sample code to predict for 2 images. #FOr single image # example of generating an image for a specific point in the latent space from keras.models import load_model from numpy import asarray from matplotlib import pyplot from numpy.random import randn from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img # load model model = load_model('malaria_augmented_model.h5') img1 = load_img('cell_validation/Parasitized/your_image1.png', target_size=(150, 150)) img2 = load_img('cell_validation/Uninfected/your_image2.png', target_size=(150, 150)) x1 = img_to_array(img1) # this is a Numpy array with shape (3, 150, 150) x2 = img_to_array(img2) x1 = x1.reshape((1,) + x1.shape) x2 = x2.reshape((1,) + x2.shape) # generate image X1 = model.predict(x1) X2 = model.predict(x2) print("Prediction for parasitized is: ", X1, " where 0 indicates parasitised and 1 indicates uninfected") print("Prediction for uninfected is: ", X2, " where 0 indicates parasitised and 1 indicates uninfected") #Parasitized, value 0 #Uninfected, value 1

Obviously it appears that that path name does not exist. So please check your current working directory and make sure the path you are defining is correct.

@@DigitalSreeni I got the ans sir thank you☺

Just checked, it works fine. Please check the spelling, keras is imported with lower case k.

@@DigitalSreeni Thanks, it worked. Do you know how to perform augmentation on a csv file type?

@@reegee8321 Hello, i tried this on spyder it works fine but when I used google colab it does not work out right example when appending to the dataset[] there are more items than the parasitized images and also the are more appended labels and just 0s can not append the 1s. What could be the issue?

I recorded many videos on techniques that can help with lung cancer detection. But I understand that you are asking for a video on the application rather than technique. I will see if I can record series of videos on various applications. Thanks for the suggestion. By the way, at work we already have prepackaged binary classification deep learning modules for training and prediction. These can be used for lung cancer detection. www.apeer.com/app/modules?page=1&q=DL%20Binary (It is free so please sign up and explore)

Not really a big believer in God but I appreciate your thoughts!

See if you can download from here: www.kaggle.com/iarunava/cell-images-for-detecting-malaria

@@DigitalSreeni yes, I was. Thanks a lot!

It has wrong i formation so I had to hide it. It will not be useful for you.