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Related Experiment Video

Updated: Jul 21, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and

Abdullah A Asiri¹, Ahmad Shaf², Tariq Ali²

¹Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia.

Life (Basel, Switzerland)

|July 29, 2023

Summary

This study introduces an advanced AI model for accurate brain tumor detection and segmentation. The fine-tuned ResNet50 and U-Net model significantly improves classification and segmentation of brain tumors using medical imaging datasets.

Keywords:

CNN ResNet50 U-Net brain tumor segmentation

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Last Updated: Jul 21, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumors are a significant global health concern, leading to high mortality rates.
Accurate early detection and classification of brain tumors are challenging due to their complex and variable nature.
Existing methods struggle with precise tumor segmentation and classification, impacting patient outcomes.

Purpose of the Study:

To develop and evaluate an improved deep learning model for accurate brain tumor detection, classification, and segmentation.
To enhance the early diagnosis of brain tumors, thereby reducing mortality.
To leverage Convolutional Neural Networks (CNNs), ResNet50, and U-Net for improved medical image analysis.

Main Methods:

A novel fine-tuned model combining CNN with ResNet50 for tumor detection and classification was proposed.
The U-Net model was integrated for precise segmentation of tumor regions.
The model was trained and validated on the publicly available TCGA-LGG and TCIA datasets comprising 120 patients.

Main Results:

The fine-tuned ResNet50 model achieved high performance metrics, including Intersection over Union (IoU) of 0.91, Dice Similarity Coefficient (DSC) of 0.95, and Similarity Index (SI) of 0.95.
The integrated U-Net with ResNet50 model demonstrated superior performance in both classifying tumor/no-tumor images and segmenting tumor regions.
The proposed model significantly outperformed other evaluated methods in accurately classifying and segmenting brain tumors.

Conclusions:

The proposed fine-tuned CNN model integrating ResNet50 and U-Net offers a robust solution for brain tumor analysis.
This AI-driven approach shows significant potential for improving the accuracy and efficiency of brain tumor diagnosis and treatment planning.
The model's high performance in classification and segmentation highlights its clinical applicability in neuro-oncology.