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?


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

One option is to use a CNN for each image, and the flatten and concatenate the results to make predictions. The CNNs can be the same or different, depending on what your goals are.
