Skin cancer is the most common type of cancer in the United States. It is the abnormal growth of skin cells, which is usually caused by the sun’s harmful rays, but this cancer can also occur on areas of skin not ordinarily exposed to sunlight. Early detection and diagnosis of skin cancer will give the greatest chance for successful skin cancer treatment. Deep learning techniques are widely used for skin cancer detection that helps in faster diagnosis.
Jayita Bhattacharyya in her blog proposed a solution to Skin Cancer Detection using TensorFlow* Optimizations from Intel, Intel® Developer Cloud, and Intel® Neural Compressor. The entire solution was implemented on 4th Gen Intel® Xeon® Processors.
The blog explains the various steps involved in the project:
- Installation of required libraries.
- Data Collection & Preparation
- Model Training
- Perform quantization using Intel Neural Compressor
- Model Testing
Read and learn more about the project at Skin Cancer Detection using TensorFlow Optimizations from Intel.
Learn more about TensorFlow Optimizations from Intel and Intel Neural Compressor!!!
TensorFlow Optimizations from Intel:
Intel collaborates with Google* to upstream most optimizations into the stock distribution of TensorFlow with the newest optimizations and features being released earlier as Intel® Extension for TensorFlow*. These optimizations can be enabled with a few lines of code and will accelerate TensorFlow-based training and inference performance on Intel CPU and GPU hardware.
Intel Neural Compressor:
Intel Neural Compressor is an open-source Python library that runs on CPUs or GPUs that performs model quantization to reduce the model size and increase the speed of deep learning inference for deployment. Intel Neural Compressor is a part of the Intel AI Analytics Toolkit. This library automates popular methods such as quantization, compression, pruning, and knowledge distillation across multiple deep learning frameworks.
What’s Next?
We encourage you to also check out and incorporate Intel’s other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel’s AI Software Portfolio to help you prepare, build, deploy, and scale your AI solutions.
For more details about the new 4th Gen Intel Xeon Scalable processors, visit Intel's AI Solution Platform portal where you can learn how Intel is empowering developers to run end-to-end AI pipelines on these powerful CPUs.
About the Author:
Jayita Bhattacharyya is currently working as an Associate Consultant at Infosys in Bengaluru, India. Her major interests are software engineering and artificial intelligence.
Product Marketing Engineer bringing cutting edge AI/ML solutions and tools from Intel to developers.