Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification

Usmani, Irfan Ahmed; Qadri, Muhammad Yasir; Zia, Razia; Alrayes, Fatma S.; Saidani, Oumaima; Dashtipour, Kia

doi:10.3390/electronics12040964

Cited by 18 publications

(7 citation statements)

References 39 publications

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“…In this scenario, larger batch sizes produce higher accuracy compared to smaller sizes. This is because a larger batch size will speed up the network computing process [ [23]]. Smaller regularization and dropout sizes produce higher accuracy.…”

Section: Results and Analysismentioning

confidence: 99%

Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning

Saraswati,

Suryawan,

Juniartini

et al. 2023

matrik

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One of the diseases that attacks the lungs is pneumonia. Pneumonia is inflammation and fluid in the lungs making it difficult to breathe. This disease is diagnosed using X-Ray. Against the darker background of the lungs, infected tissue shows denser areas, which causes them to appear as white spots called infiltrates. In the image processing approach, pneumonia-infected X-rays can be detected using machine learning as well as deep learning. The convolutional neural network model is able to recognize images well and focus on points that are invisible to the human eye. Previous research using a convolutional neural network model with 10 convolution layers and 6 convolution layers has not achieved optimal accuracy. The aim of this research is to develop a convolutional neural network with a simpler architecture, namely two convolution layers and three convolution layers to solve the same problem, as well as examining the combination of various hyperparameter sizes and regularization techniques. We need to know which convolutional neural network architecture is better. As a result, the convolutional neural network classification model can recognize chest x-rays infected with pneumonia very well. The best classification model obtained an average accuracy of 89.743% with a three-layer convolution architecture, batch size 32, L2 regularization 0.0001, and dropout 0.2. The precision reached 94.091%, recall 86.456%, f1-score 89.601%, specificity 85.491, and error rate 10.257%. Based on the results obtained, convolutional neural network models have the potential to diagnose pneumonia and other diseases.

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Section: Results and Analysismentioning

confidence: 99%

Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning

Saraswati,

Suryawan,

Juniartini

et al. 2023

matrik

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“…The goal is to acquire numerous kernel sizes inside the network rather than sequentially stacking them and ordering each to function at the same stage. Szegedy et al ( 2016 ) created the first version of the inception architecture in 2012, called GoogLeNet. The suggested model has 27 levels, including inception layers.…”

Section: Methodsmentioning

confidence: 99%

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning

Sayed,

Abd Elfattah,

Darwish

et al. 2024

Environ Sci Pollut Res

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Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart technology offers a possible alternative to traditional waste management techniques that are proving insufficient to reduce the harmful effects of trash on the environment. This paper proposes an intelligent waste classification model to enhance the classification of waste materials, focusing on the critical aspect of waste classification. The proposed model leverages the InceptionV3 deep learning architecture, augmented by multi-objective beluga whale optimization (MBWO) for hyperparameter optimization. In MBWO, sensitivity and specificity evaluation criteria are integrated linearly as the objective function to find the optimal values of the dropout period, learning rate, and batch size. A benchmark dataset, namely TrashNet is adopted to verify the proposed model’s performance. By strategically integrating MBWO, the model achieves a considerable increase in accuracy and efficiency in identifying waste materials, contributing to more effective waste management strategies while encouraging sustainable waste management practices. The proposed intelligent waste classification model outperformed the state-of-the-art models with an accuracy of 97.75%, specificity of 99.55%, F1-score of 97.58%, and sensitivity of 98.88%.

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“…During the retraining, the weights of the network are kept unchanged and only the parameters of the replaced layers are modified. Alternatively, the whole model can be retrained (unfreezing all layers) with a reduced value of the learning rate, Usmani et al [111].…”

Section: Transfer Learning In Fire Detection With Deep Learning Techn...mentioning

confidence: 99%

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Diaconu

2023

Fire

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Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed.

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Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification

Cited by 18 publications

References 39 publications

Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning

Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

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