Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer Images

Benbrahim, Houssam; Hachimi, Hanaâ; Amine, Aouatif

doi:10.12694/scpe.v21i3.1725

Cited by 10 publications

(10 citation statements)

References 25 publications

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“…On the other hand, the reference [37] used Keras on plant images to overcome the health problems, and maximum efficiency of 96.3% was achieved with just 250 images. The author [39] utilized the DCNN, TensorFlow, and Keras structures on cancer images to address skin tumors, while the study [38] employed the DL, Keras, and TensorFlow on COVID-19 identification problems, and the processors achieved 90-92% on a dataset based exclusively on X-ray images.…”

Section: Comparison and Discussionmentioning

confidence: 99%

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A Comprehensive Survey of Deep Learning Models Based on Keras Framework

Chicho

Sallow

2021

JSCDM

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Python is one of the most widely adopted programming languages, having replaced a number of those in the field. Python is popular with developers for a variety of reasons, one of which is because it has an incredibly diverse collection of libraries that users can run. The most compelling reasons for adopting Keras come from its guiding principles, particularly those related to usability. Aside from the simplicity of learning and model construction, Keras has a wide variety of production deployment options and robust support for multiple GPUs and distributed training. A strong and easy-to-use free, open-source Python library is the most important tool for developing and evaluating deep learning models. The aim of this paper is to provide the most current survey of Keras in different aspects, which is a Python-based deep learning Application Programming Interface (API) that runs on top of the machine learning framework, TensorFlow. The mentioned library is used in conjunction with TensorFlow, PyTorch, CODEEPNEATM, and Pygame to allow integration of deep learning models such as cardiovascular disease diagnostics, graph neural networks, identifying health issues, COVID-19 recognition, skin tumors, image detection, and so on, in the applied area. Furthermore, the author used Keras's details, goals, challenges, significant outcomes, and the findings obtained using this method.

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Section: Comparison and Discussionmentioning

confidence: 99%

“…Benbrahim et al [39] presented a system for classifying seven forms of skin cancer utilizing the Deep Convolution Neural Network (CNN), TensorFlow, and Keras structure. The system was put into action by using an image classification scheme on the HAM10000 database.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

Chicho

Sallow

2021

JSCDM

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“…This is particularly useful when working with images Table 1. Tensor Rank [17] b)TensorFlow's Structure…”

Section: A) A) Tensorflow Structurementioning

confidence: 99%

Disease Diagnosis Systems Using Machine Learning and Deep learning Techniques Based on TensorFlow Toolkit: A review

A.Alsalman

Khorshid

Sallow

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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Machine learning and deep learning algorithms have become increasingly important in the medical field, especially for diagnosing disease using medical databases. Techniques developed within these two fields are now used to classify different diseases. Although the number of Machine Learning algorithms is vast and increasing, the number of frameworks and libraries that implement them is also vast and growing. TensorFlow is a well-known machine learning library that has been used by several researchers in the field of disease classification. With the help of TensorFlow (Google's framework), a complex calculation can be addressed effectively by modeling it as a graph and properly mapping the graph segments to the machine in the form of a cluster. In this review paper, the role of the TensorFlow-Python framework-for disease classification is discussed.

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“…Employing a convolutional neural network (CNN) for image classification is one of the reasonable rises, and it is an essential model in developing an automatic disease diagnosis [8], [9]. Among competitors of the ImageNet challenge in 2012, the deep learning-based model AlexNet proposed by Krizhevsky et al [10] won the championship.…”

Section: Introductionmentioning

confidence: 99%

Classification-Segmentation Pipeline for MRI via Transfer Learning and Residual Networks

Duong-Trung¹,

Ha²,

Huynh³

2022

Proceedings of the Sixth International Conference on Research in Intelligent and Computing

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Artificial intelligence association into brain magnetic resonance imaging (MRI) and clinical practices embrace substantial cancer diagnosis improvement. The advancement of deep learning has improved the processing and analysis of MRI, boosting models' performance, decreasing the destructive effects of data sources overload, and increasing accurate detection and time efficacy. However, that specific dataset leads to diverse research fields such as image processing and analysis, detection, registration, segmentation, and classification. This paper proposes a decision-making pipeline for MRI data by combining image classification and segmentation. First, the pipeline should correctly produce a correct decision given an MRI image. If the figure is classified as defective, the pipeline can extract defect regions and highlight them accordingly. We have implemented several advanced convolutional neural networks with transfer learning and residual techniques to address two broad clinical concerns in one decision-making workflow.

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Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer Images

Cited by 10 publications

References 25 publications

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

Disease Diagnosis Systems Using Machine Learning and Deep learning Techniques Based on TensorFlow Toolkit: A review

Classification-Segmentation Pipeline for MRI via Transfer Learning and Residual Networks

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