Enhancing Non-Invasive Diagnosis of Endometriosis Through Explainable Artificial Intelligence: A Grad-CAM Approach
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2025, Vol 4, Issue 2
Abstract
Significant advancements in artificial intelligence (AI) have transformed clinical decision-making, particularly in disease detection and management. Endometriosis, a chronic and often debilitating gynecological disorder, affects a substantial proportion of reproductive-age women and is associated with pelvic pain, infertility, and a reduced quality of life. Despite its high prevalence, non-invasive and accurate diagnostic methods remain limited, frequently resulting in delayed or missed diagnoses. In this study, a novel diagnostic framework was developed by integrating deep learning (DL) with explainable artificial intelligence (XAI) to address existing limitations in the early and non-invasive detection of endometriosis. Abdominopelvic magnetic resonance imaging (MRI) data were obtained from the Crestview Radiology Center in Victoria Island, Lagos State. Preprocessing procedures, including Digital Imaging and Communications in Medicine (DICOM)-to-PNG conversion, image resizing, and intensity normalization, were applied to standardize the imaging data. A U-Net architecture enhanced with a dual attention mechanism was employed for lesion segmentation, while Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated to visualize and interpret the model’s decision-making process. Ethical considerations, including informed patient consent, fairness in algorithmic decision-making, and mitigation of data bias, were rigorously addressed throughout the model development pipeline. The proposed system demonstrated the potential to improve diagnostic accuracy, reduce diagnostic latency, and enhance clinician trust by offering transparent and interpretable predictions. Furthermore, the integration of XAI is anticipated to promote greater clinical adoption and reliability of AI-assisted diagnostic systems in gynecology. This work contributes to the advancement of non-invasive diagnostic tools and reinforces the role of interpretable DL in the broader context of precision medicine and women's health.
Authors and Affiliations
Afolashade Oluwakemi Kuyoro, Oluwayemisi Boye Fatade, Ernest Enyinnaya Onuiri
Keywords
Related Articles
- EP ID EP767836
- DOI https://doi.org/10.56578/ataiml040203
- Views 10
- Downloads 0
A Comprehensive Review of Ant Colony Optimization in Swarm Intelligence for Complex Problem Solving
Swarm intelligence (SI) has emerged as a transformative approach in solving complex optimization problems by drawing inspiration from collective behaviors observed in nature, particularly among social animals and insects...
Predictive Modelling of Employee Attrition Using Deep Learning
This investigation delineates an optimised predictive model for employee attrition within a substantial workforce, identifying pertinent models tailored to the specific context of employee and organisational variables. T...
Comparative Analysis of Machine Learning Algorithms for Sentiment Analysis in Film Reviews
Sentiment analysis, a crucial component of natural language processing (NLP), involves the classification of subjective information by extracting emotional content from textual data. This technique plays a significant ro...
Enhanced Real-Time Facial Expression Recognition Using Deep Learning
In the realm of facial expression recognition (FER), the identification and classification of seven universal emotional states, surprise, disgust, fear, happiness, neutrality, anger, and contempt, are of paramount import...
Characterization and Risk Assessment of Cyber Security Threats in Cloud Computing: A Comparative Evaluation of Mitigation Techniques
Advancements in information technology have significantly enhanced productivity and efficiency through the adoption of cloud computing, yet this adoption has also introduced a spectrum of security threats. Effective cybe...