A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

Abstract

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.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

“…This approach may overcome operator-based inaccuracies alongside offering improved accuracy, efficiency, and reliability. Therefore, further research aimed at enhancing the performance of this proposed method is warranted [ 8 ].…”

“…One opportunity is the use of DL to analyse histopathological images. A recent study proposed a novel CNN algorithm for predicting and diagnosing ovarian malignancies with remarkable accuracy [ 8 ]. Another opportunity is the use of DL to analyse other types of data, such as genomic data.…”

“…Traditional methods like biomarkers, biopsy, and imaging tests have been the mainstay for ovarian cancer diagnosis, but face limitations including invasiveness, limited early detection accuracy, subjectivity, and inter-observer variability. In contrast, DL leverages advanced neural networks to automatically learn abstract features from raw medical images and data, enabling more objective analysis without manually creating features that are necessary for other machine learning methods [ 8 ]. DL uses a non-linear network structure to extract features.…”

“…The integration of DL into ovarian cancer diagnosis holds immense potential for improving patient outcomes [ 8 , 14 - 16 ]. This transformative technology can assist clinicians in making more informed decisions, ultimately leading to improved treatment strategies and better patient care.…”

Screening ovarian cancer by using risk factors: machine learning assists

“…To this end, they used the advanced convolutional neural network (CNN) to stratify the malignant cells from healthy ones. Based on the results, the CNN, with an accuracy of 94% (95.12% and 93.02% for classifying cancerous and healthy cells, respectively), gained favorable performance in this respect [ 40 ]. Maria et al constructed ML models to classify OC tumors using a biomarker dataset.…”