Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Prostate cancer is a leading cause of male mortality globally.
• Accurate and timely diagnosis is crucial for optimizing patient outcomes.
• Histopathological image analysis is key for prostate cancer diagnosis.

Purpose of the Study:

• To evaluate the efficacy of the ResNet50 convolutional neural network (CNN) for classifying prostate cancer from histological images.
• To assess the diagnostic performance of ResNet50 in distinguishing benign from malignant prostate tissues.
• To compare the performance of ResNet50 against other deep learning models.

Main Methods:

• A dataset of 1276 prostate biopsy images was analyzed using the ResNet50 architecture.
• The ResNet50 model was trained to classify images as either benign or malignant.
• Performance was evaluated using metrics including accuracy, precision, recall, and F1 score.

Main Results:

• ResNet50 demonstrated excellent performance with high accuracy (0.98 for benign, 0.99 for malignant).
• Precision, recall, and F1 scores were consistently high for both benign and malignant classifications.
• The model showed a performance gain over MobileNet and CNN-RNN, with a 95% CI for accuracy of (0.91, 1.00).

Conclusions:

• The ResNet50 model shows significant potential for accurate prostate cancer classification from histological images.
• The model's robustness was confirmed through comparison with state-of-the-art deep learning models.
• Clinical integration of this AI tool could enhance decision-making and improve patient outcomes in prostate cancer management.