Lung Nodule Detection Using Polygon Approximation and Hybrid Features from CT Images

Nazir

Microscopy Res & Technique

et al. 2019

The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps—playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well‐known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Lungs nodule detection framework from computed tomography images using support vector machine

Nazir

Microscopy Res & Technique

et al. 2019

“…Zhang et al [164] used 3D skeletonization features based on the prior anatomical knowledge for the determination of the lung nodules. Naqi et al [165] used traditional manual feature HOG and CNN features to construct hybrid feature vectors to find candidate nodules. Refs.…”

Section: Lidc-idrimentioning

confidence: 99%

Survey on deep learning for pulmonary medical imaging

Song

Tian

et al. 2019

Front. Med.

As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

Concurrency and Computation

“…Such an approach improves uncertainty of the sequence generated since it integrates both good statistical properties of Pseudo Random Number Generator based on an Elliptic Curve and Pseudo Random Number Generator based on chaotic map. [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] This work proposes a new Pseudo Random Number Generator. In this scheme, double rounds of diffusion and substitution operations are performed.…”

Section: Literature Survey On Pseudo Random Number Generatormentioning

confidence: 99%

Generation of pseudo random number sequence from discrete oscillating samples of equally spread objects and application for stream cipher system

Sudeepa¹,

Aithal

2019

The demand on security privileged information has widely increased due to the sensitivity of the exchanged information over public communication channels. Nowadays, almost all organizations, mainly, business, financial, and government related areas, and also other regions are computerized, and sometimes, the data will be highly confidential. Since some areas dealing with confidential data, it is very much essential to provide the security for the data to avoid unauthorized access. Multimedia data security and security related issues have attracted many researchers towards it. Cryptography is necessary to protect the confidential information transmitting over the network. The strength of the cryptographic system, especially, stream cipher, is not only depending upon its encryption/decryption algorithm but also depends upon the randomness properties of the key sequence such as length, uniformity, independence, etc. In this work, a novel method of generating the pseudo random number that is based on the discrete equally spread oscillating points viewed orthogonally as an envelope has been considered for the generation. Depending on the number of samples, it is possible to get the higher length of the sequence. The random patterns generated by the discrete points of a moving wave are used to generate non binary random numbers. These numbers are used as key in stream cipher system. These are having greater advantages like one time padding to achieve perfect security in cryptographic application. The same non binary key sequence generated is applied for the cryptographic application, and the security parameters of the result obtained from cryptographic applications are also evaluated to observe the strength of security level. KEYWORDS discrete oscillating samples, Pseudo Random Number Generator, stream cipher system INTRODUCTIONData can be hacked during transmission through network channel. Hence, it is very essential to be transmitted in a secured manner. Cryptography is one of the major techniques to ensure security of data. Cryptography is a technique of transforming an original data into a particular form so that only those who know the key can read and process it. In cryptosystem, original message or data referred to as plain text. This could be transformed into non readable or scrambled form referred to as cipher text. This is done based on encryption algorithm. The encryption algorithm uses a key for changing the plain text into a cipher text. The cipher text can be recovered into its original form by an algorithm using a key known as decryption. The main goal of cryptography is data privacy (confidentiality), data authenticity, and data integrity (it cannot be modified in any way) in the digital world. Based on the type of key used for encryption/decryption, cryptography systems are categorized as symmetric key system and asymmetric key system. 1 The symmetric system can be categorized as stream and block cipher.Concurrency Computat Pract Exper. 2020;32:e5181. wileyonlinelibrary.com/journal/cpe Decryption Algor...