A learned 3D CNN model based on a brain MRI dataset collected from clinical cases of flat brain syndrome

Published: 22 June 2026| Version 1 | DOI: 10.17632/ybdxcbwp74.1
Contributors:
, Yoshihiro Sato,
,

Description

This trained model has been trained on a brain MRI dataset collected from clinical cases of flat brain syndrome across Japan, primarily from Showa Medical University Hospital and its affiliated medical institutions. The collection of the dataset used for training was approved by the Institutional Review Board (IRB) for Research Involving Human Subjects at Showa Medical University, and informed consent was obtained from all participants or their legal guardians. However, as the raw data cannot be made publicly available, the trained model is being released. Software/Library Version Python 3.10.18 CUDA 11.8 NumPy 1.23.5 TensorFlow 2.12.0 ''' Python code import tensorflow as tf import numpy as np # Load model Model = tf.keras.models.load_model('model_collection/ResNet18') # Show model sturacture Model.summary() # Input Data is 3D MRI data input_image = np.load('input_image_path.npy') prediction = Model(input_image.reshape((1, 64, 128, 128, 1))) print(f'prediction_class:DCX = 0, LIS1 = 1, Other = 2') print(f'prediction_result:{np.argmax(prediction)}') '''

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Machine Learning, Functional Magnetic Resonance Imaging, Convolutional Neural Network

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