Anuruk Prommakhot, Jakkree Srinonchat*
CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3313-3329, 2022, DOI:10.32604/cmc.2022.024417
- 29 March 2022
Abstract Image classification always has open challenges for computer vision research. Nowadays, deep learning has promoted the development of this field, especially in Convolutional Neural Networks (CNNs). This article proposes the development of efficiently scaled dilation of DropBlock optimization in CNNs for the fungus classification, which there are five species in this experiment. The proposed technique adjusts the convolution size at 35, 45, and 60 with the max-polling size 2 × 2. The CNNs models are also designed in 12 models with the different BlockSizes and KeepProp. The proposed techniques provide maximum accuracy of 98.30% for the training More >
Login
Register
