AI and image recognition are making great progress in medical imaging, but they still have some serious challenges when it comes to diagnosing tumors. These systems depend heavily on the quality of the data they're trained on. So if the dataset isn’t diverse enough, the model might not work well in real-life situations. They also rely a lot on image preprocessing, which can sometimes distort important features like tumor boundaries.

Another issue is segmentation: tumors don’t always have clear edges, so even advanced techniques struggle without manual input. And while deep learning models like CNN–LSTM can achieve high accuracy, they’re often black boxes, making it hard for doctors to trust the results without understanding how they were reached.

Our recent work on Alzheimer’s detection (published in Algorithms, 2024) tackled similar problems by combining a smart segmentation pipeline with a hybrid CNN–LSTM model. While it worked well for brain scans, the challenges we faced; like data quality, model interpretability, and the need for clinical validation, apply just as much to tumor diagnosis.

Dear Ashikur Rahman Nazil ,

AI and ML are pivotal in transforming cancer treatment through enhanced image analysis. By improving early detection, characterizing tumours, and personalizing treatment, these technologies offer significant benefits in the fight against cancer. As advancements continue, the integration of AI/ML in oncology promises to enhance patient outcomes further and streamline cancer care processes.

https://www.excelra.com/whitepaper/ai-ml-medical-imaging-cancer-diagnosis-treatment/

Regards,

Shafagat

AI and image recognition have revolutionized tumor diagnosis, yet significant barriers remain. Challenges include inconsistent imaging quality, lack of annotated multimodal datasets, and difficulty in generalizing across diverse tumor types and populations. Moreover, explainability and physician trust remain critical for clinical integration, as do regulatory constraints and risks tied to overreliance on black-box models in life-critical decisions.

In my paper “Lightweight RegNet-Driven Framework for Neurodegenerative Disease Classification” (IJAESR, 2023), we tackled model complexity, artifact sensitivity, and interpretability—challenges equally relevant in tumor imaging. Similarly, “Neurofusion” integrates MRI and PET using feature fusion to improve diagnostic consistency, yet also exposes the trade-offs in multimodal data alignment and explainability—mirroring the exact hurdles faced in AI-driven tumor diagnosis.

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