Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial...

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Oncology
• Machine Learning for Diagnostics

Background:

• Brain tumors are a leading cause of death globally, necessitating accurate and timely diagnostic tools.
• Existing machine learning and deep learning methods for brain tumor detection often lack precision and speed.
• Abnormal brain tissue growth characterizes tumors, with potential for metastasis.

Purpose of the Study:

• To develop a more precise brain tumor detection method using artificial intelligence.
• To enhance the accuracy and timeliness of brain tumor diagnosis through advanced deep learning.
• To propose a novel conditional generative adversarial network (CGAN) integrated with a convolutional neural network (CNN).

Main Methods:

• Utilized a conditional generative adversarial network (CGAN) framework.
• Integrated a generator and discriminator within the CGAN architecture.
• Fine-tuned a convolutional neural network (CNN) using CGAN outputs for enhanced detection.
• Employed a publicly available Kaggle dataset of brain tumor MRI images for experimentation.

Main Results:

• The proposed CGAN model demonstrated high performance on two datasets.
• Achieved an accuracy of 0.93 on Dataset 1 and 0.97 on Dataset 2.
• Evaluated using statistical metrics including precision, specificity, sensitivity, and F1-score.
• Outperformed existing techniques in brain tumor detection accuracy.

Conclusions:

• The conditional generative adversarial network (CGAN) offers a promising approach for precise brain tumor detection.
• The integration of CGAN with CNN fine-tuning significantly improves diagnostic accuracy.
• This AI-driven method has the potential to enhance clinical decision-making in neuro-oncology.