Advanced AI-Assisted Medical Device for Cancer Diagnosis, Monitoring, and Treatment
Creators: name: Bensalem, Ahmed ilies
Publisher: name: Zenodo
Year: 2024

description:  



 



AI-Based Medical Device for Diagnostic and Therapeutic Assistance in Optimal Cancer Management

Abstract

Each year, millions of cancer-related deaths occur worldwide, often resulting from incorrect diagnoses regarding the type and subtype of cancer. Misdiagnosis leads to inappropriate treatments and unnecessary invasive procedures. Our innovative medical device addresses these challenges by providing comprehensive assistance throughout the diagnostic and therapeutic process. By integrating image analysis modules for both mammography and pathology, the system accurately identifies the exact cancer type and subtype while offering tailored treatment recommendations via a virtual medical expert trained on an extensive database of medical and oncological knowledge. The system’s dual architecture—drawing inspiration from advanced deep learning frameworks such as DeepSeek—has achieved an accuracy exceeding 89% for mammographic anomaly classification (using the CBIS-DDSM dataset augmented with GANs) and 85.9% for histopathological image analysis (using the BRACS dataset). This advanced system holds the potential to save millions of lives and alleviate patient suffering by optimizing both diagnosis and treatment.



Introduction

Cancer remains one of the leading causes of death globally, with millions of fatalities each year. One of the major factors contributing to these outcomes is the misclassification of cancer types and subtypes during diagnosis—especially when analyses are based on mammography and histopathological images. Variability in human interpretation can lead to inappropriate treatments and unnecessary invasive procedures.

Recent advances in artificial intelligence (AI) and deep learning offer promising solutions for enhancing diagnostic accuracy. Our work tackles these challenges by developing a dual-module medical device that operates at two critical levels:



Mammography Module: An advanced YOLO v11 extra model is employed to detect and classify 16 distinct anomaly classes—including Architectural Distortion, Asymmetry, Mass, and Microcalcification (each subdivided into BI-RADS 2 to 5)—thereby reducing the frequency of unnecessary anatomical pathology exams.

Pathology Module: Inspired by architectures such as DeepSeek, this component analyzes histopathological images via a deep learning model (e.g., an optimized version of ResNet-50) trained on the BRACS dataset. It provides a detailed classification of cancer types and subtypes, along with expert treatment recommendations.


Thus, the central challenge addressed in this work is: How can we improve cancer diagnostic precision to prevent misclassification of cancer subtypes and avoid inappropriate treatments, ultimately saving millions of lives and alleviating patient suffering ?



Materials and Methods

1. Device Description

The proposed device integrates several components:





Imaging Unit:



Mammography: A high-resolution camera combined with an adjustable zoom lens (up to 400× magnification) is used to capture detailed mammographic images.

Pathology: A digital microscope coupled with a miniature endoscope (manufactured via 3D printing) captures high-quality images of tissue samples.





User Interface:



An interactive touchscreen displays images and diagnostic outputs in a clear and intuitive manner.





Development Board and Connectivity:



An embedded processing board equipped with a Wi-Fi module facilitates local image processing through USB interfaces and secure data transmission to a remote server.





AI Expert Server:



This dedicated server hosts trained AI models for image analysis and generates diagnostic recommendations. It leverages an extensive database of medical and oncological knowledge, incorporating an architecture inspired by DeepSeek.




2. Data Acquisition and Preprocessing

Data are sourced from two primary channels:



Mammographic Images: Obtained primarily from the CBIS-DDSM dataset.

Histopathological Images: Derived from the BRACS dataset.


Preprocessing includes:



Normalization: Standardizing image sizes and formats.

Enhancement: Improving image quality through contrast adjustment and noise reduction.

Data Augmentation: Applying classical techniques (rotations, translations, etc.) alongside GAN-based augmentation to enrich the CBIS-DDSM dataset.


3. Mammography Diagnostic Module

The mammography component employs a YOLO v11 extra model configured to detect and classify 16 anomaly classes as detailed below:



Architectural Distortion: BI-RADS 2, 3, 4, 5

Asymmetry: BI-RADS 2, 3, 4, 5

Mass: BI-RADS 2, 3, 4, 5

Microcalcification: BI-RADS 2, 3, 4


Training on the CBIS-DDSM dataset, together with GAN-augmented augmentation techniques, has enabled this module to achieve an overall accuracy exceeding 89%. This high level of diagnostic precision minimizes the need for unnecessary anatomical pathology procedures.

4. Pathology Diagnostic Module

The pathology module is designed for the analysis of histopathological images—an inherently challenging task even for seasoned professionals. Utilizing a deep learning architecture inspired by DeepSeek (for example, an optimized version of ResNet-50), the module is trained on the BRACS dataset. It provides:



Precise classification of both cancer types and subtypes.

An accuracy of 85.9% as demonstrated in preliminary tests.

Tailored therapeutic recommendations generated by an AI-driven virtual expert system.




Results

Preliminary evaluations reveal that:





Mammography Module:



The integration of GAN-based augmentation with the YOLO v11 extra model yielded a classification accuracy exceeding 89% for the 16 defined anomaly classes, substantially reducing misdiagnoses and unnecessary invasive procedures.





Pathology Module:



The deep learning classifier, inspired by DeepSeek, achieved an accuracy of 85.9% in differentiating between various cancer types and subtypes, sometimes outperforming expert human interpretation.




These results indicate that the joint operation of the two modules provides a comprehensive diagnostic and therapeutic decision-support system that not only enhances diagnostic reliability but also optimizes treatment strategies—thereby saving lives and alleviating patient suffering .



Discussion

The global burden of cancer mortality is significantly exacerbated by diagnostic errors and subsequent suboptimal treatments. Our device’s dual-modular, AI-driven approach addresses this critical issue by:



Reducing Diagnostic Errors: The mammography module detects subtle anomalies that are often overlooked in conventional imaging assessments.

Enhancing Treatment Decisions: The pathology module not only classifies cancer subtypes with high precision but also offers tailored therapeutic recommendations.

Minimizing Invasive Procedures: By delivering reliable, refined diagnostic outputs, the system reduces unnecessary biopsies and other invasive interventions.

Saving Lives: With its high diagnostic accuracy, the system has the potential to save millions of lives and alleviate patient suffering (تخفيف معانات المرضى).


This approach, inspired by advanced architectures such as DeepSeek, represents a significant advance toward more personalized and effective cancer care. Future work will involve extensive clinical validation and the integration of additional imaging modalities to further refine system performance.



Conclusion

We have presented an innovative AI-based medical device that integrates dual diagnostic modules for optimal cancer management. Combining a YOLO v11 extra-based mammography analyzer (trained on the CBIS-DDSM dataset with GAN-enhanced augmentation) with a DeepSeek-inspired pathology classifier (trained on the BRACS dataset), our system achieves diagnostic accuracies of over 89% and 85.9%, respectively. This comprehensive solution not only improves diagnostic precision compared to conventional methods but also provides tailored therapeutic recommendations, thus reducing inappropriate treatments. Ultimately, this device holds significant promise in saving millions of lives and alleviating patient suffering  by substantially enhancing both the diagnostic and therapeutic management of cancer.



Note: The preliminary findings presented herein require extensive clinical validation. Future studies will aim to incorporate additional imaging modalities and clinical feedback to further optimize the performance of this innovative diagnostic and therapeutic system.