A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women
Blessed Ziyambe,
Abid Yahya,
Tawanda Mushiri,
Muhammad Usman Tariq,
Qaisar Abbas,
Muhammad Babar,
Mubarak Albathan,
Muhammad Asim,
Ayyaz Hussain,
Sohail Jabbar
Affiliations
Blessed Ziyambe
Department of Electrical Engineering, Harare Polytechnic College, Causeway Harare P.O. Box CY407, Zimbabwe
Abid Yahya
Department of Electrical, Computer and Telecommunications Engineering, Botswana International University of Science and Technology, Palapye 10071, Botswana
Tawanda Mushiri
Department of Industrial and Mechatronics Engineering, Faculty of Engineering & the Built Environment, University of Zimbabwe, Mt. Pleasant, 630 Churchill Avenue, Harare, Zimbabwe
Muhammad Usman Tariq
Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
Qaisar Abbas
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Muhammad Babar
Robotics and Internet of Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia
Mubarak Albathan
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Muhammad Asim
EIAS Data Science Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia
Ayyaz Hussain
Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
Sohail Jabbar
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.
