Cervical Cancer Prediction and Classification Using Deep Learning on Medical Image Data

IEEE BASE PAPER TITLE:

A Novel Web Framework for Cervical Cancer Detection System: A Machine Learning Breakthrough

IEEE BASE PAPER ABSTRACT:

Cervical cancer, the second most prevalent cancer among women worldwide, is primarily attributed to the human papillomavirus (HPV). Despite advances in healthcare, it remains a significant cause of mortality among women across diverse regions, surpassing other hereditary cancers. Early detection is pivotal, as survival rates exceed 90% when the disease is identified in its early stages. In response to this critical need, we introduce WFC2DS (Web Framework for Cervical Cancer Detection System), a novel expert web system specifically designed to revolutionize cervical cancer diagnosis. WFC2DS integrates a sophisticated ensemble of machine learning classification algorithms, including Artificial Neural Network (ANN), AdaBoost, K-Nearest Neighbor (KNN), Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). This ensemble approach enables a comprehensive analysis of a large dataset comprising information from 858 patients with 36 attributes, with the primary objective being the early detection of cervical cancer, using the last attribute, Biopsy, as the target variable. Our evaluation criteria encompass accuracy, specificity, sensitivity, and the F1 score. Among the algorithms, RFC and DT emerge as the most promising, demonstrating exceptional performance with an accuracy of 98.1% and an F1 score of 0.98. AdaBoost shows an accuracy of 97.4% and an F1 score of 0.98, ANN attains an accuracy of 97.7% and an F1 score of 0.96, SVM achieves an accuracy of 96.2% and an F1 score of 0.96, and KNN reaches an accuracy of 90.6% with an F1 score of 0.91. This research significantly contributes to reducing the global burden of cervical cancer, emphasizing transformative advancements in women’s healthcare. WFC2DS, with its cutting-edge machine learning techniques, not only improves the accuracy of cervical cancer diagnosis but also enhances the overall healthcare landscape for women worldwide.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Xception Architecture.

OUR PROPOSED PROJECT ABSTRACT:

Cervical cancer is one of the leading causes of cancer-related deaths among women worldwide. Cervical cancer remains a significant global health challenge, with early detection being pivotal for reducing mortality rates. Early and accurate detection is critical for effective treatment and improved survival rates. This project, titled “Cervical Cancer Prediction and Classification Using Deep Learning on Medical Image Data,” aims to leverage advanced deep learning techniques for automated and precise classification of cervical cancer stages using medical images.

Developed with Python as the backend, HTML, CSS, and JavaScript for the frontend, and Flask as the web framework, the system integrates the Xception architecture—a state-of-the-art deep learning model known for its exceptional performance in image classification tasks. The model was trained and validated on the Herlev dataset, comprising 17,304 images across seven distinct classes: Carcinoma In Situ (SCCSI), Mild Dysplasia (MS-NKD), Moderate Dysplasia (MOS-NKD), Severe Dysplasia (SS-NKD), Columnar (CE), Intermediate Squamous (ISE), and Superficial Squamous (SSE). The dataset was split into 12,110 training images, 3,111 validation images, and 2,083 test images.

The Xception-based model achieved an impressive training accuracy of 93.00% and a validation accuracy of 93.00%, demonstrating its effectiveness in distinguishing between different stages of cervical cell abnormalities. This system provides a reliable and scalable tool that can assist pathologists by improving diagnostic accuracy, reducing manual workload, and enabling faster cervical cancer screenings. By integrating state-of-the-art deep learning with medical imaging, this project advances the potential for technology-driven solutions in healthcare.

This work underscores the importance of adopting deep learning techniques in the early diagnosis of cervical cancer, offering significant benefits in clinical decision-making and patient care.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 8 GB.

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.12.0.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

MIMONAH AL QATHRADY, AHMAD SHAF, TARIQ ALI, UMAR FAROOQ, AQIB REHMAN, SAMAR M. ALQHTANI, MOHAMMED S. ALSHEHRI, SULTAN ALMAKDI, MUHAMMAD IRFAN, SAIFUR RAHMAN, AND LADON AHMED BADE ELJAK, “A Novel Web Framework for Cervical Cancer Detection System: A Machine Learning Breakthrough”, in IEEE Access, vol. 12, pp. 41542-41556, 2024.

Python IEEE Projects

Cervical Cancer Prediction and Classification Using Deep Learning on Medical Image Data