SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification

• URL: http://arxiv.org/abs/2403.18870v3
• Date: Sat, 16 Nov 2024 11:36:01 GMT
• Title: SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification
• Authors: Md. Simul Hasan Talukder, Sharmin Akter, Abdullah Hafez Nur, Mohammad Aljaidi, Rejwan Bin Sulaiman, Ali Fayez Alkoradees,
• Abstract summary: sugarcaneNet2024 is a unique model that outperforms previous methods for automatically and quickly detecting sugarcane disease. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models. This optimized sugarcaneNet2024 model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100%.
• Score: 0.46180371154032906
• License:
• Abstract: Sugarcane, a key crop for the world's sugar industry, is prone to several diseases that have a substantial negative influence on both its yield and quality. To effectively manage and implement preventative initiatives, diseases must be detected promptly and accurately. In this study, we present a unique model called sugarcaneNet2024 that outperforms previous methods for automatically and quickly detecting sugarcane disease through leaf image processing. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models, particularly InceptionV3, InceptionResNetV2, DenseNet201, DenseNet169, Xception, and ResNet152V2. Initially, we added three more dense layers with 0.0001 LASSO regularization, three 30% dropout layers, and three batch normalizations with renorm enabled at the bottom of these pre-trained models to improve the performance. The accuracy of sugarcane leaf disease classification was greatly increased by this addition. Following this, several comparative studies between the average ensemble and individual models were carried out, indicating that the ensemble technique performed better. The average ensemble of all modified pre-trained models produced outstanding outcomes: 100%, 99%, 99%, and 99.45% for f1 score, precision, recall, and accuracy, respectively. Performance was further enhanced by the implementation of an optimized weighted average ensemble technique incorporated with grid search. This optimized sugarcaneNet2024 model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100% , respectively.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Comparative Performance Analysis of Transformer-Based Pre-Trained Models for Detecting Keratoconus Disease [0.0]
  This study compares eight pre-trained CNNs for diagnosing keratoconus, a degenerative eye disease. MobileNetV2 was the best accurate model in identifying keratoconus and normal cases with few misclassifications.
  arXiv  Detail & Related papers  (2024-08-16T20:15:24Z)
• Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping [2.0661578265672094]
  Multi-resolution graph models leverage the spatial relationships of patches at multiple magnifications, learning the context for each patch. We conduct the most thorough validation of a graph model for ovarian cancer subtyping to date. The accuracy of the combined foundation model and multi-resolution graph network offers a step towards the clinical applicability of these models.
  arXiv  Detail & Related papers  (2024-07-25T15:08:54Z)
• Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks [65.23740556896654]
  We investigated whether self-supervised pretraining could produce a neural network feature extractor applicable to multiple classification tasks in lung ultrasound analysis. When fine-tuning on three lung ultrasound tasks, pretrained models resulted in an improvement of the average across-task area under the receiver operating curve (AUC) by 0.032 and 0.061 on local and external test sets respectively.
  arXiv  Detail & Related papers  (2023-09-05T21:36:42Z)
• PotatoPestNet: A CTInceptionV3-RS-Based Neural Network for Accurate Identification of Potato Pests [0.0]
  We propose an efficient PotatoPestNet AI-based automatic potato pest identification system. We leveraged the power of transfer learning by employing five customized, pre-trained transfer learning models. Among the models, the Customized Tuned Inception V3 model, optimized through random search, demonstrated outstanding performance.
  arXiv  Detail & Related papers  (2023-05-27T17:38:16Z)
• (Certified!!) Adversarial Robustness for Free! [116.6052628829344]
  We certify 71% accuracy on ImageNet under adversarial perturbations constrained to be within a 2-norm of 0.5. We obtain these results using only pretrained diffusion models and image classifiers, without requiring any fine tuning or retraining of model parameters.
  arXiv  Detail & Related papers  (2022-06-21T17:27:27Z)
• Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time [69.7693300927423]
  We show that averaging the weights of multiple models fine-tuned with different hyper parameter configurations improves accuracy and robustness. We show that the model soup approach extends to multiple image classification and natural language processing tasks.
  arXiv  Detail & Related papers  (2022-03-10T17:03:49Z)
• To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection [95.21700830273221]
  Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. We propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions. We prove that even a much smaller dataset with well-matched annotations can facilitate models to achieve better performance as well as generalizability.
  arXiv  Detail & Related papers  (2021-09-04T02:45:22Z)
• A Critical Review of the state-of-the-art on Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes [0.0]
  This article compares ten recently proposed neural networks and proposes two ensemble neural network-based models for blood glucose prediction. We compare their performance using the most common metrics in blood glucose prediction and rank the best-performing ones using three methods devised for the statistical comparison. Our analysis highlights those models with the highest probability of being the best predictors, estimates the increase in error of the models that perform more poorly with respect to the best ones, and provides a guide for their use in clinical practice.
  arXiv  Detail & Related papers  (2021-09-02T09:08:26Z)
• Effective Model Sparsification by Scheduled Grow-and-Prune Methods [73.03533268740605]
  We propose a novel scheduled grow-and-prune (GaP) methodology without pre-training the dense models. Experiments have shown that such models can match or beat the quality of highly optimized dense models at 80% sparsity on a variety of tasks.
  arXiv  Detail & Related papers  (2021-06-18T01:03:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.