Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet

Cano, Elia; Mendoza-Avilés, José; Areiza, Mariana; Guerra, Noemi; Mendoza-Valdés, José Longino; Rovetto, Carlos

doi:10.7717/peerj-cs.371

Cited by 20 publications

(8 citation statements)

References 36 publications

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“…In this study, we tested nine CNNs that have been applied and performed well in medical image classification (23)(24)(25)(26)(27)(28). The Alexnet architecture won the 2012 ImageNet Large Scale Visual Recognition Challenge, and since then CNNs have been flourishing.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks

Wang

Chen

et al. 2022

Front. Surg.

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BackgroundMicrotia is a congenital abnormality varying from slightly structural abnormalities to the complete absence of the external ear. However, there is no gold standard for assessing the severity of microtia.ObjectivesThe purpose of this study was to develop and test models of artificial intelligence to assess the severity of microtia using clinical photographs.MethodsA total of 800 ear images were included, and randomly divided into training, validation, and test set. Nine convolutional neural networks (CNNs) were trained for classifying the severity of microtia. The evaluation metrics, including accuracy, precision, recall, F1 score, receiver operating characteristic curve, and area under the curve (AUC) values, were used to evaluate the performance of the models.ResultsEight CNNs were tested with accuracy greater than 0.8. Among them, Alexnet and Mobilenet achieved the highest accuracy of 0.9. Except for Mnasnet, all CNNs achieved high AUC values higher than 0.9 for each grade of microtia. In most CNNs, the grade I microtia had the lowest AUC values and the normal ear had the highest AUC values.ConclusionCNN can classify the severity of microtia with high accuracy. Artificial intelligence is expected to provide an objective, automated assessment of the severity of microtia.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks

Wang

Chen

et al. 2022

Front. Surg.

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“…NASNet (Neural Architecture Search Network) is a family of DNNs that was proposed by a reinforcement learning method to find an optimum NN structure [21]. The objective of NASNet is to automatically design NN architecture that performs better than human-designed architecture.…”

Section: Nasnet Modelmentioning

confidence: 99%

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

Ashary,

Jambi,

Ashari

et al. 2023

CSSE

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Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste, enhancing waste management and reducing environmental pollution. Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data and metrics. Therefore, this study designs an Entropy-based Feature Fusion using Deep Learning for Waste Object Detection and Classification (EFFDL-WODC) algorithm. The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects. In the presented EFFDL-WODC system, two major procedures can be contained, such as waste object detection and waste object classification. For object detection, the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone network. In addition, entropy feature fusion-based models such as VGG-16, SqueezeNet, and NASNet models are used. Finally, the EFFDL-WODC technique uses a graph convolutional network (GCN) model performed for the classification of detected waste objects. The performance validation of the EFFDL-WODC approach was validated on the benchmark database. The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

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“…Data imbalance is a well-known scenario in medical imaging when training using a deep neural network ( Johnson & Khoshgoftaar, 2019 ; Cano et al, 2021 ). Zhang et al (2022) used a balanced total data sample for training and testing to identify three bacteria classes to overcome this problem.…”

Section: Evolution Of Cell Analysis Approachesmentioning

confidence: 99%

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

Zamani

Zaki

Hamid

et al. 2022

PeerJ

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Background and Aims A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning.

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Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet

Cited by 20 publications

References 36 publications

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

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