Aspergillus niger Fungus Detection using Transfer Learning Technique and Modified Backpropagation Algorithm with Inertia and Legendre Polynomial
Looking at the loss due to health problems from fungal diseases in one hand and the benefits from its industrial/agricultural use, rapid automated fungal species identification is the need of the hour. Hence, proposed a fast identification of fungal species by a 15 minutes staining procedure followed by an artificial-intelligence-based image classification technique. In this modern era, deep architectures have shown a significant performance on computer vision problems. Instead of developing a new model from scratch, the pre-trained convolutional neural network models are available to obtain the appropriate features from input samples using the transfer-learning technique. This work utilizes the transfer-learning approach for feature extraction and classification performed using the proposed modified third-term Backpropagation (BP) algorithm. This proposed algorithm contains Inertia as a third factor in the weight updation rule expanded in the form of the Legendre polynomial to overcome the limitations of the traditional Backpropagation algorithm. The effectiveness of the proposed classifier compared to the results of the existing cutting-edge algorithms namely, Backpropagation algorithm, Backpropagation algorithm using Momentum, and softmax classifier. Compare to the existing models, the proposed model scored a high testing accuracy of 97.27%.
Confusion matrix, Fungus classification, Orthogonal polynomial, Pre-trained deep learning models
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