Transfer learning based histopathologic image classification for breast cancer detection

Deniz, Erkan; Şengür, Abdulkadir; Kadiroğlu, Zehra; Guo, Yanhui; Bajaj, Varun; Budak, Ümit

doi:10.1007/s13755-018-0057-x

Cited by 283 publications

(131 citation statements)

References 10 publications

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“…The current study fine-tuned this by using pretrained CNN models based on transfer learning. The reason for using pretrained CNN models is that they are faster and easier than training a CNN model with randomly initialized weights [35]. In addition, the fine-tuning process is based on transferring new layers instead of the last three layers of the pretrained networks to our classification task, as shown in Figure 2.…”

Section: Transfer Learningmentioning

confidence: 99%

Plant disease and pest detection using deep learning-based features

Türkoğlu¹,

Hanbay²

2019

Turk J Elec Eng & Comp Sci

210

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The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers.

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Section: Transfer Learningmentioning

confidence: 99%

Plant disease and pest detection using deep learning-based features

Türkoğlu¹,

Hanbay²

2019

Turk J Elec Eng & Comp Sci

210

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“…In future works, the deep learning achievements will be investigated on the EEG rhythms (Deniz et al, 2018). Besides, the achievements of all EEG channels will be investigated in the deep learning fashion.…”

Section: Discussionmentioning

confidence: 99%

Two Stepped Majority Voting for Efficient EEG based Emotion Classification

Ismael

Alçın

Abdalla

et al. 2020

Preprint

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In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG based emotion classification. Emotion recognition is important for human-machine interactions. Facial-features and body-gestures based approaches have been generally proposed for emotion recognition. Recently, EEG based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG based emotion classification.

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“…For this work, the last three layers are replaced: a fully-connected layer, a softmax layer, and a classification output layer. The main purpose of using pre-trained CNN models is related to the fast and easy training of a CNN using randomly initialized weights [29], as well achievement of lower training error than ANNs that are not pre-trained [30].…”

Section: Data Trainingmentioning

confidence: 99%

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

et al. 2020

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Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases. This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset used for the experiments is contained by nine different classes of tomato diseases and a healthy class from PlantVillage. The models were evaluated through a multiclass statistical analysis based on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned.

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Transfer learning based histopathologic image classification for breast cancer detection

Cited by 283 publications

References 10 publications

Plant disease and pest detection using deep learning-based features

Plant disease and pest detection using deep learning-based features

Two Stepped Majority Voting for Efficient EEG based Emotion Classification

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

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