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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.

Adeel Ahmed Abbasi¹, Lal Hussain¹, Imtiaz Ahmed Awan¹

¹Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan.

Cognitive Neurodynamics

|July 14, 2020

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This study introduces a deep learning approach using GoogleNet for improved prostate cancer detection from MRI scans. The convolutional neural network (CNN) model significantly outperformed traditional machine learning methods in accuracy.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Prostate cancer is a leading cause of cancer death in the US, with detection challenges due to complex MRI findings.
Existing diagnostic techniques often lack sufficient efficiency for accurate prostate cancer identification.
Radiologists face difficulties in precisely detecting prostate cancer from medical images.

Purpose of the Study:

To develop and evaluate a robust deep learning model for enhanced prostate cancer detection.
To compare the performance of a convolutional neural network (CNN) against traditional machine learning algorithms.
To assess the efficacy of a transfer learning approach in improving diagnostic accuracy.

Main Methods:

A deep learning convolutional neural network (CNN) model, specifically GoogleNet, was employed using a transfer learning strategy.

Keywords:

Convolutional neural network (CNN)Deep learning (DL)GoogleNet Prostate cancer Transfer learning

Related Experiment Videos

Last Updated: Dec 15, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Machine learning classifiers including Decision Tree, Support Vector Machine (SVM) with various kernels, and Bayes were utilized for comparison.

Features such as Morphological, Entropy-based, Texture, SIFT, and Elliptic Fourier Descriptors were extracted from the cancer MRI database for classifier training.

Main Results:

The deep learning CNN model (GoogleNet) utilizing transfer learning achieved the highest performance in prostate cancer detection.
Machine learning classifiers demonstrated reasonably good results, but were outperformed by the deep learning technique.
Performance evaluation included metrics like specificity, sensitivity, positive predictive value, and receiver operating characteristic (ROC) curves.

Conclusions:

Deep learning techniques, particularly the GoogleNet CNN model with transfer learning, offer superior performance for prostate cancer detection compared to conventional machine learning methods.
The study highlights the potential of advanced AI in improving the accuracy and efficiency of cancer diagnosis from MRI.
Further research and implementation of such models could significantly aid radiologists in diagnosing prostate cancer more effectively.