Deep Neural Network Based Classification of Tumourous and Non-tumorous Medical Images

Makde, Vipin; Bhavsar, Jenice; Jain, Swati; Sharma, Priyanka

doi:10.1007/978-3-319-63645-0_22

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…DL solutions have been applied to medical image analysis [120] in very different topics related to classification or detection, such as identifying lung nodules into benign or malignant [121], alcoholism identification through brain MRI [122,123], multiple sclerosis detection [124], categorizing chest X-rays into different diseases [125], distinguishing patients with Alzheimer's disease versus normal [126], diagnosing diabetic retinopathy using digital photographs of the fundus of the eye [127], discriminating kidney cancer histopathological images into tumor or non-tumor [128], detecting cancerous lung nodules [129] and lung cancer stage [130] on CT scans, malignant skin cells on dermatological photographs [131], mitotic figures in breast histology images [132], or cell nuclei in colorectal adenocarcinoma histology image [133]. Regarding image segmentation, DL covers a variety of organs such as liver, prostate, spine, and knee cartilage both in CT and MRI [116,134].…”

Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…V. Makde et al [11] proposed two frameworks of CNN (Alexnet and ZFNet), to train the system for tumor detection in lung nodules as well as brain tumor. This study used two different datasets such as Lung CT image and Brain MRI image with obtaining more than 97 % of the accuracy in classification training data.…”

Section: Literature Reviewmentioning

confidence: 99%

Lung cancer classification with Convolutional Neural Network Architectures

Mohammed

Çınar

2021

QAJ

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One of the most common malignant tumors in the world today is lung cancer, and it is the primary cause of death from cancer. With the continuous advancement of urbanization and industrialization, the problem of air pollution has become more and more serious. The best treatment period for lung cancer is the early stage. However, the early stage of lung cancer often does not have any clinical symptoms and is difficult to be found. In this paper, lung nodule classification has been performed; the data have used of CT image is SPIE AAPM-Lung. In recent years, deep learning (DL) was a popular approach to the classification process. One of the DL approaches that have used is Transfer Learning (TL) to eliminate training costs from scratch and to train for deep learning with small training data. Nowadays, researchers have been trying various deep learning techniques to improve the efficiency of CAD (computer-aided system) with computed tomography in lung cancer screening. In this work, we implemented pre-trained CNN include: AlexNet, ResNet18, Googlenet, and ResNet50 models. These networks are used for training the network and CT image classification. CNN and TL are used to achieve high performance resulting and specify lung cancer detection on CT images. The evaluation of models is calculated by some matrices such as confusion matrix, precision, recall, specificity, and f1-score.

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“…Makde et al explored the challenging tasks in tumour identification and classification of various medical images. They proposed a novel technique based on deep learning named as CNN [5]. This method helps in identifying the tumour and also used for classifying the tumour cells in the medical images.…”

Section: Related Workmentioning

confidence: 99%

“…Existing methods do not provide sufficient accuracy in classifying the lung CT images. The constraint in the conventional classifiers like artificial neural networks, deep neural networks, bagged random tree (BRT), and convolutional neural networks (CNNs) [5–7] do not provide efficient accuracy when applied on lung CT images. The proposed classification technique bag of visual words (BoVW) provides much better accuracy than the existing classification techniques attempted on lung CT images.…”

Section: Introductionmentioning

confidence: 99%

Novel computer‐aided diagnosis of lung cancer using bag of visual words to achieve high accuracy rates

Chellan¹,

Chellappan

2018

J. eng.

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Deep Neural Network Based Classification of Tumourous and Non-tumorous Medical Images

Cited by 10 publications

References 8 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Lung cancer classification with Convolutional Neural Network Architectures

Novel computer‐aided diagnosis of lung cancer using bag of visual words to achieve high accuracy rates

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