Web Scraping for Ovarian Cancer Detection: Utilizing Open-Source Whisper AI for Identifying Relevant Terminology and Improving Early Diagnosis
Keywords:
Automatic Speech Recognition, chemical named entity recognition, Ovarian Cancer, Natural language processing, Web ScrapingAbstract
This research paper investigates the effectiveness of automatic speech recognition (ASR) using OpenAI Whisper module in detecting chemical word entities related to ovarian cancer from human speech. Ovarian cancer is a deadly disease that requires early detection for successful treatment. The proposed ASR system is based on deep learning models capable of recognizing complex speech patterns and distinguishing between different chemical terms related to ovarian cancer. Moreover, the detected chemical entities are used for web content search and retrieval, which can help in discovering useful information related to ovarian cancer. This study highlights the potential of using ASR technology for early detection and accurate identification of ovarian cancer-related chemical entities and utilizing them for retrieving relevant information from the web and opens new avenues for developing intelligent systems for disease diagnosis and treatment.
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