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LIFE SAVING TIMELY BREAST CANCER DETECTION
Client’s Need
- Ease workload and pressure on radiologists.
Enhanced diagnostic accuracy and efficiency.
Ability to classify into benign or malignant tumors.
Goals of the project
- Classifier capable of accurately categorizing mammograms as benign or malignant.
- Enhance diagnostic efficiency by automating the mammogram analysis process, reducing workload and pressure on radiologists.
- Achieve high-speed classification of mammograms to facilitate timely detection and diagnosis of breast cancer.
- Provide radiologists with a tool that aids in confidently identifying and diagnosing breast cancer with accuracy comparable to expert radiologists.
Our Approach
Deployment of enhanced computer vision technology with Convolutional Neural Network (CNN)-based deep learning classifier trained on mammogram datasets for automated breast cancer detection.
Optimize model architecture and training parameters to achieve high-speed and accurate classification.
Deep learning based accuracy improvement for differentiating benign and malignant tumors.
Outcome
96% detection accuracy and in classification (Malignant or Benign)
Expert level analysis results in 3 seconds
Ability to host on cloud and mobile apps, enabling access of this life saving service to far fledged patients
Significant improvements in Radiologist productivity and accuracy
Timely detection enables early health care, saving lives.
Published:
September 28, 2024
Category:
Computer Vision
Client:
Oceanthemes
