- Accurately differentiating between benign and malignant lung cancer cells in histopathological biopsy images poses a significant challenge in medical diagnosis.
- This obstacle delays timely and effective lung cancer type detection, ultimately affecting patient care and treatment outcomes.
- Manual assessment by pathologists can be time-consuming and subject to human error, impacting timely and effective treatment.
- Developing a computer vision model for automated lung cancer detection using biopsy images aims to enhance diagnostic accuracy, expedite the evaluation process, and improve patient care outcomes.
- Enhancing the model's interpretability to aid medical professionals in understanding the reasoning behind predictions.
- Extending the model to classify specific lung cancer subtypes, allowing for even more targeted treatment approaches.
- Integrating real-time analysis capabilities to assist in intraoperative decision-making during surgeries.
- Collaborating with pathologists to continuously improve the model's performance through iterative updates and training on diverse datasets.
- Expanding the framework to encompass other cancer types, thereby contributing to a comprehensive diagnostic tool for various malignancies.
- Create a highly accurate machine learning model capable of distinguishing between benign and malignant lung cancer cells in histopathological images.
- Enable healthcare professionals to leverage the model's predictions for timely diagnosis, leading to tailored treatment plans and enhanced patient outcomes.
- Establish a versatile framework for further expansion, encompassing the potential to refine classification accuracy, incorporate subtype classification, and contribute to ongoing research in lung cancer detection