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Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network

Madhura Kalbhor¹, Swati Shinde¹, Daniela Elena Popescu²

¹Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune 411044, India.

Diagnostics (Basel, Switzerland)

|April 13, 2023

Summary

This study introduces a hybrid deep learning model for accurate cervical cancer detection from Pap-smear images, achieving 95.33% accuracy with ResNet-50. This approach aims to reduce false positives in cancer diagnosis.

Keywords:

convolutional neural networks cytology image classification fuzzy min–max neural network (FMMN)machine learning pre-trained models transfer learning

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Area of Science:

Computer Vision
Artificial Intelligence
Medical Imaging

Background:

Medical image analysis aids healthcare professionals in disease prediction.
Pap-smear tests are crucial for cervical cancer screening but suffer from high false-positive rates due to human error.
Deep learning models show promise for accurate medical image classification.

Purpose of the Study:

To develop and evaluate a novel hybrid technique for accurate Pap-smear image classification.
To combine deep learning architectures with a fuzzy min-max neural network for enhanced feature extraction and classification.
To improve the accuracy of computer-aided diagnostic systems for early cervical cancer detection.

Main Methods:

A hybrid approach integrating deep learning models (Alexnet, ResNet-18, ResNet-50, GoogleNet) with a fuzzy min-max neural network was proposed.
Pre-trained deep learning models were utilized for feature extraction from Pap-smear images.
The fuzzy min-max neural network was employed for classification of extracted features.
Experiments were conducted on the Herlev and Sipakmed benchmark datasets.

Main Results:

The highest classification accuracy achieved was 95.33% using the fine-tuned ResNet-50 architecture on the Sipakmed dataset.
Alexnet also demonstrated strong performance on the Sipakmed dataset.
The hybrid model leveraged the advantages of fuzzy min-max neural network classifiers, including efficient training and handling of overlapping classes.

Conclusions:

The proposed hybrid technique effectively classifies Pap-smear images, offering a significant improvement over traditional methods.
The integration of deep learning and fuzzy min-max neural networks provides a robust solution for computer-aided cervical cancer diagnosis.
This research contributes to the development of more accurate and reliable diagnostic tools for healthcare professionals.