Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images

Budak, Ümit; Cömert, Zafer; Rashid, Zryan Najat; Şengür, Abdulkadir; Çıbuk, Musa

doi:10.1016/j.asoc.2019.105765

Cited by 121 publications

(69 citation statements)

References 21 publications

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“… Fully-connected layers: This layer is used to transfer the activations that are obtained by passing the data throughout the network for the next unit. Fully connected layers are located at the end of the architecture to ensure the connections between all activations and computational nodes in these layers [27] , [28] , [29] , [30] . These layers are exploited when the CNNs are used as the feature extractors.…”

Section: Methodsmentioning

confidence: 99%

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Nour

Cömert

Polat

2020

Applied Soft Computing

Self Cite

276

215

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A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases.

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Section: Methodsmentioning

confidence: 99%

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Nour

Cömert

Polat

2020

Applied Soft Computing

Self Cite

276

215

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“…The fully connected layer connects all neurons of the previous and next layers to each other. The values of neurons give information about the extent to which a value matches the particular class [36] . The values in last fully connected layer is conveyed to the softmax layer, which gives the probable scores of classes.…”

Section: Techniques and Methodsmentioning

confidence: 99%

DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images

Demir

2021

Applied Soft Computing

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The new coronavirus, known as COVID-19, first emerged in Wuhan, China, and since then has been transmitted to the whole world. Around 34 million people have been infected with COVID-19 virus so far, and nearly 1 million have died as a result of the virus. Resource shortages such as test kits and ventilator have arisen in many countries as the number of cases have increased beyond the control. Therefore, it has become very important to develop deep learning-based applications that automatically detect COVID-19 cases using chest X-ray images to assist specialists and radiologists in diagnosis. In this study, we propose a new approach based on deep LSTM model to automatically identify COVID-19 cases from X-ray images. Contrary to the transfer learning and deep feature extraction approaches, the deep LSTM model is an architecture, which is learned from scratch. Besides, the Sobel gradient and marker-controlled watershed segmentation operations are applied to raw images for increasing the performance of proposed model in the pre-processing stage. The experimental studies were performed on a combined public dataset constituted by gathering COVID-19, pneumonia and normal (healthy) chest X-ray images. The dataset was randomly separated into two sections as training and testing data. For training and testing, these separations were performed with the rates of 80%–20%, 70%–30% and 60%–40%, respectively. The best performance was achieved with 80% training and 20% testing rate. Moreover, the success rate was 100% for all performance criteria, which composed of accuracy, sensitivity, specificity and F-score. Consequently, the proposed model with pre-processing images ensured promising results on a small dataset compared to big data. Generally, the proposed model can significantly improve the present radiology based approaches and it can be very useful application for radiologists and specialists to help them in detection, quantity determination and tracing of COVID-19 cases throughout the pandemic.

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“…The RNN model has been extensively studied in the field of language and text recognition [ 22 ]. The LSTM neural network model, optimized on the basis of RNN [ 23 ], has achieved good results in areas such as semantic analysis and image recognition that require strong historical information memory [ 24 , 25 , 26 ], but it is rarely used in the field of physiological data analysis. Time series data inevitably discuss cyclical and non-cyclical issues.…”

Section: Introductionmentioning

confidence: 99%

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Song

Gao

Zhao

et al. 2020

Sensors

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In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.

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Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images

Cited by 121 publications

References 21 publications

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

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