I’m a software engineer and graphic designer who bridges creativity with technology.

github

Jennifer Lewis  • Peerlist

Acknowledging the current limitations of breast cancer detection using machine learning techniques, with a detection rate at 50%, this application aims to provide users with access to early detection capabilities. We utilized mammogram images from datasets such as INbreast and CBIS-DDSM, containing a diverse range of breast mammography images including masses, benign, normal, and cancer volumes.

Our model builds upon the ResNet-50 architecture, a model pre-trained on the ImageNet dataset, providing robust capabilities in deciphering images with low signal-to-noise ratios. We further enhanced the ResNet-50 by chaining it with other models, such as the CNN, to improve its performance in breast cancer detection.

Detect Breast Cancer in High Density Breast T

- Researched quantum computing algorithms, formulated an objective function for the Nursing Scheduling Problem, which was the domain for the project. 
- Utilized and tested quantum algorithms through Cloudslab's IQuantum resource simulation with MQTBench 
- Implemented an nurse assigner in Qiskit. 
- Applied agile practices and gathered feedback from the client for requirements engineering.
- Designed the user interface and date model for the application.