Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms

Tiwari, Laxmikant; Raja, Rohit; Awasthi, Vineet Kumar; Miri, Rohit; Sinha, G. R.; Alkinani, Monagi H.; Polat, Kemal

doi:10.1016/j.measurement.2020.108882

Cited by 55 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With an unified technique, numerous organizations may identify various illnesses with a lung X-ray. Another well-liked study area in CT is the identification of topographical features suggestive of interstitial pulmonary disorders [ 186 – 188 ].…”

Section: Anatomical Domains Of Medical Imagesmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

show abstract

Section: Anatomical Domains Of Medical Imagesmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…There is limited research on utilizing Convolutional Neural Networks (CNNs) to analyze EBUS images, and distinguishing between benign and potentially cancerous tumors based solely on EBUS images is challenging [ 97 ].…”

Section: Evolving Techniques For Lung Cancer Detectionmentioning

confidence: 99%

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

et al. 2023

View full text Add to dashboard Cite

One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

show abstract

“…Initially, reservoirs are determined by looking at the shape of an image through touching digits. These reservoirs are divided into top and bottom reservoirs [38], [39]. The shape of the reservoir and the position of the reservoir's foundation define the touching positions of the digits.…”

Section: A Water Reservoir Segmentationmentioning

confidence: 99%

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

Raja

Gupta

Miri

2021

EJERS

Self Cite

View full text Add to dashboard Cite

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

show abstract

Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms

Cited by 55 publications

References 29 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

Contact Info

Product

Resources

About