The document discusses acquiring medical imaging datasets containing 3564 slices from 233 meningioma patients, 1426 slices from pituitary tumor patients, and 930 slices from glioma patients. It describes renaming and merging the datasets into a single dataset with 3564 slices of 6mm thickness for segmentation and classification using various deep learning models, including U-Net, U-Net with classification, R2U-Net, and DeepLab V3. The models are evaluated on a test set, and their results, including classification reports, confusion matrices, and metrics like loss, dice coefficient, and accuracy are analyzed and compared.

1. ACQUIRING DATA
SETS
3064 slices
set
-
Publicly available kaggle data
meningioma
from 233 patients, containing
and
slices),
1426
slices), glioma (
708
(
slices)
930
pituitary tumor (
Publicly available data-set (500
slices) from 63 patients.
Renaming and merging both dataset (new
data 3564 with slice thickness is 6 mm )
Choosing the models to be
implanted on the data set
U – NET segmentation
U – NET with
classification
R2U-NET
Deep Lab V3
Pre- processing
Threshold the mask to
[0,1] values only
Normalize the images
to range [0-1]
Implement the models and add branch of
dense layers for classification to the
network
Save all models with results as zip file
Evaluate the models on the test set and
visualize the result
Compare all models, create classification reports, confusion matrix plot the loss, dice coef and accuracy
DATA SETS
MODELS
MODELS
OPERATION
COMPEAR
AMONG
MODELS