Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.

Early analysis is a necessity for the more effective treatment of cancers. In gynaecological cancers, like endometrial, ovarian, and cervical cancers, the current efforts are aimed at discovering new analytical biomarkers to help decrease the global health burden related to these cancers. In cervical cancer, efficient screening is highly suggested for the prevention of invasive cancer occurrence and death. However, the interpretation of medical images for gynaecological cancer remains prone to human error. Artificial intelligence-based solutions offer numerous medical image challenges to help with the clinical decision support process. This paper presents a Deep Neural Architecture Empowered for Accurate Diagnosis of Gynaecological Cancer (DNAE-ADGC) model using medical imaging. The primary purpose of the paper is to empower gynaecological cancer diagnostics by developing an accurate, efficient, and intelligent detection framework using advanced techniques. Initially, the image pre-processing stage employs a two-level approach named adaptive filter that contains Median-Modified Wiener Filter (MMWF) and Cross Guided Bilateral Filter (CGBF). Followed by, the MobileNetV3Large model was deployed for feature extraction process. Besides, the DNAE-ADGC algorithm is applies the graph convolutional network and gated recurrent unit (GCN-GRU) network for detecting and classifying gynaecological cancer. At last, the explainable artificial intelligence (XAI) technique applies Grad-CAM to develop the transparency, interpretability, and reliability of AI models. The comparative analysis of the DNAE-ADGC method demonstrated an improved accuracy value of 97.92% with other methodologies under the Malhari dataset.