Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

Barburiceanu, Stefania; Meza, Serban; Orza, Bogdan; Măluţan, Raul; Terebes, Romulus

doi:10.1109/access.2021.3131002

Cited by 66 publications

(19 citation statements)

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“…Feature extraction of the IHC images from pre-trained CNNs was taken from the fully-connected layers that combined features of the input images over all spatial locations. This layer selection for extracting features from pre-trained CNNs was reported as being effective for classification tasks by several previous studies [35] , [36] , [37] , [38] ,…”

Section: Methodsmentioning

confidence: 95%

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Pham

Ravi

Fan

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Background: Over a decade, tissues dissected adjacent to primary tumors have been considered ''normal'' or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers. Methods: This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs. Results: Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods. Conclusion: Preliminary results not only add objective evidence to recent findings of NATs' molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. Clinical impact: The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.

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Section: Methodsmentioning

confidence: 95%

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Pham

Ravi

Fan

et al. 2023

IEEE J. Transl. Eng. Health Med.

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“…Sabino et al [15] proposed a multilayer network for texture feature extraction, but faced computational issues. Barburiceanu et al [16] extracted textures from deep learning models, but lacked region specification. Anwer et al [17] combined LBP with a deep learning model to create TEX-Nets, but the fusion did not consider specific image regions.…”

Section: Methodsmentioning

confidence: 99%

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

Veerashetty

2023

RAiSE-2023

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“…The authors [19] in their study have discussed on using pre-trained AlexNet model and have claimed that the accuracy is obtained at reduced training time. They have also discussed due to similarity in visual disease symptom misclassification is highly probable in corn and tomato disease categories.…”

Section: Mixup Data Augmentation-tomato Disease Classesmentioning

confidence: 99%

EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

Devi¹,

Kumar²,

Sivakumar³

2023

Computer Systems Science and Engineering

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Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network's topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model's learning capacity. This provides a better interpretability of the model's understanding on the test domains. With these insights, our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach. This Progressive Learning for TL (PL-TL) is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species, 5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms, though it is not a benchmark dataset. To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms, Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes. Also, the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3, and Vision Transformer a non-CNN model. It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%, 97.5%, 80.1% on Cassava, PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy, precision, f1-score is also studied.

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Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

Cited by 66 publications

References 50 publications

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

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