Coupling of a lightweight model of reduced convolutional autoencoder with linear SVM classifier to detect brain tumours on FPGA

In the field of computer assisted diagnosis, classification of tumours using lightweight machine learning algorithms require a complete detection chain of pattern recognition process. The diagnosis system can be made portable by using Field Programmable Gate Arrays (FPGAs) which accommodates the cap...

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Veröffentlicht in:	Expert systems with applications Jg. 290; S. 128444
Hauptverfasser:	Chatterjee, Soumita, Pandit, Soumya, Das, Arpita
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 25.09.2025
Schlagworte:	Brain Tumour Feature maps FPGAs Linear SVM Loop unrolling Reduced Convolutional Autoencoder FPGAs Feature maps Reduced Convolutional Autoencoder Linear SVM Brain Tumour Loop unrolling
ISSN:	0957-4174
Online-Zugang:	Volltext
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