RETRACTED ARTICLE: An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Quantum Computing

Background:

• Brain tumors are a leading cause of death globally, necessitating early and accurate detection for effective treatment.
• Conventional machine learning methods struggle with precise brain tumor detection using MRI images.
• Quantum computing and deep learning offer potential advancements in medical diagnostics.

Purpose of the Study:

• To propose a novel technique, Lee sigma filtered histogram segmentation (LSFHS), for accurate and time-efficient brain tumor detection.
• To overcome the limitations of conventional machine learning in MRI-based brain tumor identification.
• To enhance early-stage brain tumor diagnosis through improved accuracy and speed.

Main Methods:

• The LSFHS technique involves preprocessing using an adaptive Lee sigma filter to reduce noise.
• Gray bimodal histogram segmentation is applied to partition the preprocessed MRI images.
• Feature extraction and classification using a TanH activation function are performed for tumor detection.

Main Results:

• The LSFHS technique demonstrated a 14% increase in tumor detection accuracy.
• Tumor detection time was reduced by 25% compared to existing methods.
• The error rate in tumor detection was reduced by 58%.

Conclusions:

• The LSFHS technique significantly outperforms state-of-the-art methods in brain tumor detection accuracy and speed.
• LSFHS enables earlier detection of brain tumors with higher precision.
• This novel approach offers a promising solution for improving brain tumor diagnosis using MRI.