Enhancing the detection of airway disease by applying deep learning and explainable artificial intelligence

Koul, Apeksha; Bawa, Rajesh K.; Kumar, Yogesh

doi:10.1007/s11042-024-18381-y

Cited by 4 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Loss, however, is a way to measure how much the predicted values differ from the actual values. Lower values indicate that the model is performing better (24) .…”

Section: Performance Metricsmentioning

confidence: 99%

“…Recall, or sensitivity, measures the model's capacity to correctly identify all relevant instances by computing the ratio of true positives to the sum of true positives and false negatives. F1 score represents the harmonic mean of precision and recall, providing a balanced assessment that is especially valuable when there is an imbalance between disease and non-disease instances (24) .…”

Section: Performance Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Plant Disease Detection using Advanced Deep Learning Models

Kaur,

Bansal

2024

IJST

View full text Add to dashboard Cite

Objective: The aim of this paper is to analyze the efficacy of employing multiple advanced convolutional neural networks (CNNs) for the purpose of enhancing the accuracy in detecting and classifying various plant diseases. Methods: The research involves the analysis of 7623 training images as well as 1906 validation images of different plant diseases and employed advanced deep learning models like DenseNet169, Xception, InceptionV3, MobileNetV2, and ResNet50V2 to classify them. At first the RGB images are converted to Grayscale and later to enhance their quality, few techniques have been used such as Otsu thresholding, noise removal, distance transform, and watershed techniques. Subsequently, contour features are extracted by calculating morphological values to obtain the necessary region that correspond to diseased areas in plant images. Findings: On evaluating the performance of the applied models on the basis of various metrics, MobileNetV2 and ResNetV2 achieved the highest validation accuracy scores of 99.42% each, with their respective loss values of 0.19 and 0.49. In terms of recall, precision, and F1 score, all models, except MobileNetV2 and InceptionV3, attained optimal scores of 0.99 each. Novelty: The novelty of this paper resides in the incorporation of multiple image segmentation techniques with fine tuning the parameters of advanced Convolutional Neural Network (CNN) models on the basis of various factors such as the number of images, size, channel, classes etc to generate the optimal results. Keywords: Agriculture, Plant diseases, Artificial Intelligence, Advanced CNN models, Watershed Technique

show abstract

“…Loss, however, is a way to measure how much the predicted values differ from the actual values. Lower values indicate that the model is performing better (24) .…”

Section: Performance Metricsmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%