Regularized Dense Layered LeNet towards Mushroom Type Classification
M. Shyamala Devi, K C Harivishnu, Jaya Suriya, Kalana Jayasuriya, Infant Jason G A, Farih Muhammad · 2024
Among the edible varieties of fungi, mushrooms possess the strongest nutrients found in the plant. Nevertheless, because of their great demand for food and significant benefits to medical research, it is imperative to identify the different varieties of mushrooms within the current species. The deep learning technology has recently rendering the CNN models towards classification of various object in interdisciplinary fields. This paper proposes Regularized Dense Layered LeNet (RDL-LeNet) that categorizes the mushroom types with high accuracy. The Mushrooms Classification Dataset, which includes 3150 mushroom pictures was used for this execution. The Mushrooms Classification dataset was fitted with the existing CNN models to select the best CNN model. To determine the most efficient CNN framework, the Mushrooms Classification dataset was fitted using the available CNN models. When compared to the other CNN model, the LeNet performs better, with an accuracy rate of 90.67%. LeNet has now been chosen to adjust the quantity of dense layers. LeNet typically consist of three convolutions, two pooling layers, and two dense layers. In order to suggest the recommended framework RDL-LeN et, the LeNet is now changed by adding an additional dense layer that is made up of a single block of batch normalization, relu optimization, and drop out at the end that further compresses the output into vector size of 84. The RDL-LeNet is made up of three convolutional layers, two pooling layers, and three dense layers. The third dense layer consists of a single block that is made up of batch normalization, relu optimization, and dropout, ultimately leading to the final output layer. The suggested RDL-LeNet and the current CNN have been implemented with the Mushroom Classification Dataset. The experiment demonstrates that the suggested RDL-LeNet model works better in the categorization of mushroom types, with a high accuracy of 98.68%.