AI Article Synopsis
- This work introduces dedicated hardware for real-time cancer detection using FPGA technology.
- The system employs a combination of Multilayer Perceptron (MLP) Artificial Neural Networks and Digital Image Processing techniques to analyze skin lesions and classify them as melanoma or non-melanoma.
- The effectiveness of the hardware is evaluated based on execution time, resource usage, and power consumption, and compared to a similar software implementation on an ARM A9 microprocessor.
Article Abstract
This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313700 | PMC |
| http://dx.doi.org/10.3390/s20113168 | DOI Listing |
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