Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization

Malliga, S.; Prasad, L V Narasimha; Janakiramaiah, B.; Babu, A. Mohan; Sathishkumar, V E

doi:10.1089/big.2021.0218

Cited by 51 publications

(17 citation statements)

References 28 publications

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“…A cascade of classification models was trained for prediction of treatment response rate in the 30% to 50% range, or at least 30%, response rate in the 50% to 75% range, or at least 50%, and response rate over 75%, at 6, 9 and 12 months, based on the selected features, with model accuracy of not less than 70%. The classification algorithms used were random forests and the hyperparameters were chosen on a heuristic basis using a Bayesian search optimization method [35,36]. The optimization metric chosen was F1 score [37].…”

Section: Machine Learning and Statistical Analysismentioning

confidence: 99%

Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

González-Martínez

Pagán

Sanz-García

et al. 2022

Euro J of Neurology

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Background and purpose: Several variables have been reported to be associated with anti-calcitonin gene-related peptide (CGRP) receptor or ligand antibody response, but with differing results. Our objective was to determine whether machine-learning (ML)based models can predict 6-, 9-and 12-month responses to anti-CGRP receptor or ligand therapies among migraine patients. Methods:We performed a multicenter analysis of prospectively collected data from patients with migraine receiving anti-CGRP therapies. Demographic and clinical variables were collected. Response rates in the 30% to 50% range, or at least 30%, in the 50% to 75% range, or at least 50%, and response rate of at least 75% regarding the reduction in the number of headache days per month at 6, 9 and 12 months were calculated. A sequential forward feature selector was used for variable selection and ML-based predictive models for the response to anti-CGRP therapies at 6, 9 and 12 months, with model accuracy not less than 70%, were generated.

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Section: Machine Learning and Statistical Analysismentioning

confidence: 99%

Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

González-Martínez

Pagán

Sanz-García

et al. 2022

Euro J of Neurology

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“…[da Rocha et al 2020] proposed the hyperparameter optimization based on the Bayesian method and evaluated AlexNet, ResNet50, and SqueezeNet CNNs trained with data augmentation and transfer learning. Similarly, [Subramanian et al 2022a] and [Subramanian et al 2022b] also applied Bayesian optimization and tested a dataset of images obtained from different sources. [Haque et al 2022] evaluated three CNN architecture inspired by InceptionV3 to identify maize leaf diseases in images collected in India.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction

Antico¹,

Rodrigues

Moreira³

2022

Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2022)

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The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.

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“…Deep learning models can not only find cancer earlier, but they can also improve detection accuracy. Deep learning based computer vision algorithms specializing in image recognition have been applied to medical imaging techniques such as CT and MRI scans [2], [3], [4], [5]. Several attempts have been made to extract image information, including spatial correlations, via medical imaging-based deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multiple Types of Cancer Classification Using CT/MRI Images Based on Learning Without Forgetting Powered Deep Learning Models

Subramanian¹,

Cho

Sathishkumar

et al. 2023

IEEE Access

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Cancer is the second biggest cause of death worldwide, accounting for one of every six deaths. On the other hand, early detection of the disease significantly improves the chances of survival. The use of Artificial Intelligence (AI) to automate cancer detection might allow us to evaluate more cases in less time. In this research, AI-based deep learning models are proposed to classify the images of eight kinds of cancer, such as lung, brain, breast, and cervical cancer. This work evaluates the deep learning models, namely Convolutional Neural Networks (CNN), against classifying images with cancer traits. Pre-trained CNN variants such as MobileNet, VGGNet, and DenseNet are employed to transfer the knowledge they learned with the ImageNet dataset to detect different kinds of cancer cells. We use Bayesian Optimization to find the suitable values for the hyperparameters. However, transfer learning could make it so that models can no longer classify the datasets they were initially trained. So, we use Learning without Forgetting (LwF), which trains the network using only new task data while keeping the network's original abilities. The results of the experiments show that the proposed models based on transfer learning are more accurate than the current state-of-the-art techniques. We also show that LwF can better classify both new datasets and datasets that have been trained before.

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Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization

Cited by 51 publications

References 28 publications

Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction

Multiple Types of Cancer Classification Using CT/MRI Images Based on Learning Without Forgetting Powered Deep Learning Models

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