# Brain tumour detection using CNN

I have a fairly basic mathematical and implementational understanding of ML algorithms and CNNs, and I am trying to think of an approach for this task: https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/data?select=test.

The "data" section explains the task and also gives a preview of the dataset.

1) Doubts on general Implementation approach.

From what I understand, we have 4 input parameters: FLAIR , T1W, T1Gd, and T2W. (call them $$x_{1}$$,$$x_{2}$$,$$x_{3}$$ and $$x_{4}$$). Based on these 4 parameters, we have to compute the "MGMT status"(Presence of MGMT), which is binary, i,e takes on values ($$0/1$$).

We can then use a CNN architecture that has $$1,x1,x2,x3,x4$$ in it's input layer, and uses a sigmoid activation function (to get the output in $$(0,1)$$).

However, this approach works if the $$x_{i}$$s were numeric values. However, in my case, the parameters FLAIR , T1Gd etc are in the form of images.

Now, I am aware that images are fed to neural networks as inputs, eg. in object detection programs, however in those examples a single image is fed as an input, and features are subsequently extracted by the network.

How should I approach my particular case, where I have multiple images as input parameters?

2) Understanding the dataset

I am also quite confused regarding the layout of the dataset given on the website.

What exactly do the top level folders, which are names 00000,00001...etc represent? They cant be the "patient no.", as these folders then contain the FLAIR,T1W...folders, which consist of many images. Had the folder been representing the patient numbers, (i.e. training examples), the parameter folders would each consist of only a single image....

Also, the file train_labels.CSV contains the column heading "BraTS21ID". What exactly does this correspond to in the dataset?