Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection

Khan, Muhammad Attique; Rubab, Saddaf; Kashif, Asifa; Sharif, Muhammad; Muhammad, Nazeer; Shah, Jamal Hussain; Zhang, Yudong; Satapathy, Suresh Chandra

doi:10.1016/j.patrec.2019.11.014

Cited by 119 publications

(37 citation statements)

References 28 publications

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“…Furthermore, the irregularities and complexities associated with varied shapes and sizes of haemorrhagic lesions with time will also make the process more difficult and strenuous. Moreover, the process can become a laborious and daunting task, particularly in large clinical settings, which can introduce inadvertent error and delay [10][11][12][13][14]. This can cause additional morbidity and even mortality to the patient.…”

Section: Introductionmentioning

confidence: 99%

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Raghavendra

Pham

Gudigar

et al. 2021

Complex Intell. Syst.

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Brain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions.

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Section: Introductionmentioning

confidence: 99%

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Raghavendra

Pham

Gudigar

et al. 2021

Complex Intell. Syst.

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“…Improving the graphic features of an image is the primary objective of contrast enhancement. It is a preprocessing step that is used in many applications like biomedical imaging, agriculture infections diagnosis, and some others [ 37 , 38 , 39 , 40 , 41 , 42 ]. The impact of low contrast images is not useful for feature extraction, as visually, tumors are not visible and error prone.…”

Section: Proposed Methodologymentioning

confidence: 99%

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

et al. 2020

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Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

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“…Therefore, quality training and robust algorithms are required for improved accuracy. In the medical domain, state-of-the-art ML techniques exist, which worked exceptionally for different kinds of problems [15] , [16] , [17] , [18] . However, COVID19-pneumonia is diagnosed using chest CT scans, which are collected from 10 patients [19] , [20] , [21] , [22] , [23] , [24] .…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine

Khan

Kadry

Zhang

et al. 2021

Computers & Electrical Engineering

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120

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Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection

Cited by 119 publications

References 28 publications

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine

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