Architecture of Deep Learning Algorithms in Image Classification: Systematic Literature Review
Keywords:
Feature extraction, deep learning, deep neural network architectures, convolution neural network, transfer learningAbstract
The number of data points predicted correctly out of the total data points is known as accuracy in image classification models. Assessment of the accuracy is very important since it compares the correct images to the ones that have been classified by the image classification models. Image classification accuracy is a challenge since image classification models classify images to the class they don’t belong to hence there is an inaccurate relationship between the predicted class and the actual class which results in a low model accuracy score. Therefore, there is a need for a model that can classify the images with the highest accuracy. The paper presents image classification models together with the feature extraction methods used to classify maize disease images. The researcher used an augmented maize leaf disease dataset obtained from the Kaggle website. Features are extracted from maize disease images and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction methods and the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The experimental outcome also indicated that the Artificial Neural Network model had the highest accuracy of 0.82 compared to Logistic Regression, K-Nearest Neighbors, Random Forest, Linear Support Vector Classifier, Decision Tree, and Support Vector Machine.
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