Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Paredes, Ofelia Roque; Sierra-Liñan, Fernando; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael

doi:10.14569/ijacsa.2022.0130963

Cited by 19 publications

(18 citation statements)

References 20 publications

(26 reference statements)

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“…The VGG16 model also achieved satisfactory results in terms of 88% accuracy. However, this model, achieved a better performance in [14], reaching 95.70% accuracy in tumor detection in monograph images and the VGG19 model in this work achieved a performance of 86% accuracy, higher than that achieved in [15], where it reached 72% pressure in pneumonia detection through transfer learning with CNN. Artificial intelligence, specifically neural networks, have contributed significantly to the clinical field, models such as ResNet50 and Inception-v3, are great and efficient predictors in this field of health, and in this work have been classified as the best models in performance and accuracy, to identify and classify breast cancer using transfer learning page.…”

Section: Resultsmentioning

confidence: 58%

“…The performance of the proposed convolutional network structure they evaluated and compared with existing algorithms. In [15], four convolutional neural network (CNN) models were proposed for pneumonia detection in chest radio-graphs. They were trained to classify radiographs into two types: normal and pneumonia, using multiple convolutional layers.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

Guevara-Ponce,

Roque-Paredes,

Zerga-Morales

et al. 2023

IJACSA

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Breast cancer is the leading cause of mortality in women worldwide. One of the biggest challenges for physicians and technological support systems is early detection, because it is easier to treat and establish curative treatments. Currently, assistive technology systems use images to detect patterns of behavior with respect to patients who have been found to have some type of cancer. This work aims to identify and classify breast cancer using deep learning models and convolutional neural networks (CNN) with transfer learning. For the breast cancer detection process, 7803 real images with benign and malignant labels were used, which were provided by BreaKHis on the Kaggle platform. The convolutional basis (parameters) of pre-trained models VGG16, VGG19, Resnet-50 and Inception-V3 were used. The TensorFlow framework, keras and Python libraries were also used to retrain the parameters of the models proposed for this study. Metrics such as accuracy, error ratio, precision, recall and f1-score were used to evaluate the models. The results show that the models based on VGG16, VGG19 ResNet-50 and Inception-V3 obtain an accuracy of 88%, 86%, 97% and 96% respectively, recall of 84%, 82%, 96% and 96% respectively, in addition to f1-score of 86%, 83%, 96% and 95% respectively. It is concluded that the model that shows the best results is Resnet-50, obtaining high results in all the metrics considered, although it should be noted that the Inception-V3 model achieves very similar results in relation to Resnet-50, in all the metrics. In addition, these two models exceed the 95% threshold of correct results.

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Section: Resultsmentioning

confidence: 58%

Section: Related Workmentioning

confidence: 99%

Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

Guevara-Ponce,

Roque-Paredes,

Zerga-Morales

et al. 2023

IJACSA

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“…It uses backpropagation to train the network [34]. The MLP is composed of multiple layers, each of which is connected to all the others, forming a directed network [35]. The MLP learns a feature from a set of inputs and combines the various features into a set of outputs [36].…”

Section: A Multi-layer Perceptronmentioning

confidence: 99%

“…As a nonparametric supervised learning classifier, the K-NN algorithm uses proximity to perform classifications and predictions to perform classifications and predictions, respectively [35]. The algorithm stores the attribute vectors and labels used during its training phase so that the algorithm can be retrained [36].…”

Section: B K-nearest Neighbormentioning

confidence: 99%

Breast Cancer Prediction using Machine Learning Models

Iparraguirre-Villanueva¹,

Epifanía-Huerta²,

Torres-Ceclén³

et al. 2023

IJACSA

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Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.

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“…Twitter is the platform where users share their reactions, feelings, and opinions related to epidemics such as COVID-19, monkeypox, and other epidemics [ 12 , 13 ]. However, to collect all this valuable information provided by Twitter, the use of algorithms based on machine learning (ML) is required, due to a large number of words and contextual phrases that this represents for its processing [ 14 , 15 ]. Sentiment analysis is an NLP technique used to determine positive, negative, and neutral data.…”

Section: Introductionmentioning

confidence: 99%

The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model

Iparraguirre-Villanueva

Álvarez-Risco

Salazar

et al. 2023

Vaccines

Self Cite

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Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus.

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Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

Cited by 19 publications

References 20 publications

Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

Breast Cancer Prediction using Machine Learning Models

The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model

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