An Early Prediction of Lung Cancer using CT Scan Images

Mr, Aaron Maurer

doi:10.53759/181x/jcns202101008

Cited by 20 publications

(4 citation statements)

References 12 publications

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“…The use of the multi-modality approach can be considered further, and advanced machine learning techniques based on a hybrid/ensemble approach can be explored to classify BC using relevant markers identified from multimodal data. The proposed approach can also be implemented to detect cancers of skin [41], lung [42], and brain tumor [43].…”

Section: Results Of Proposed Lstm-based Deep Learning Approachmentioning

confidence: 99%

Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

Atrey

Singh

Bodhey

2023

Braz. arch. biol. technol.

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The existing practice for Breast Cancer (BC) characterization includes histopathological analysis, which is tedious and time-consuming due to massive data analysis. Further, such techniques are subjected to inter-and intra-observer variability due to the non-availability of skilled pathologists, particularly in low resource settings. Thus, we propose a multi-feature classification technique for risk stratification of BC in Histopathology Images (HI) using machine learning strategies and a Long Short-Term Memory (LSTM) based deep learning approach. Experiments are performed on a publicly available HI database from which a total of 658 image features are extracted, while 192 relevant features are obtained after feature selection using genetic algorithm. The highest accuracy of 99.85% using 192 features under the 5-fold data division protocol is obtained with the LSTM approach. The proposed framework for analyzing HI using multiple grayscale and color features showed promising results and can be an effective tool in the histopathology laboratory.

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Section: Results Of Proposed Lstm-based Deep Learning Approachmentioning

confidence: 99%

Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

Atrey

Singh

Bodhey

2023

Braz. arch. biol. technol.

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“…These results justified our choice of techniques for the identification and classification of breast mammograms. The proposed method can also be useful for other types of cancers such as skin cancer [ 48 ], lung cancer [ 49 ], and brain tumor [ 50 ] detection.…”

Section: Discussionmentioning

confidence: 99%

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.

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“…The framework has 83.8% accuracy, 82.6% sensitivity, and 86.8% specificity. The advantage of this version is that it utilizes around channel inside the Region of interest (ROI) extraction stage, which decreases the rate of preparing and acknowledgment steps [7]. Although the implementation value is reduced, it has however unacceptable accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Lung Cancer Using CT Scan Images Based on Artificial Intelligence

Sadhin

Woishe

Sultana

et al. 2022

Malaysian J. Sci. Adv. Tech.

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Lung cancer is among the leading cause of death among men and women. Early detection of lung cancers can increase the possibility of survival amongst patients. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected on time. Computerized tomography (CT) is frequently used for diagnosis and is more efficient than X-ray. However, the images need to be reviewed by a qualified physician who specializes in interpreting the CT scan. This may lead to misinterpretation and conflicting reports among physicians. Therefore, a lung cancer detection system that uses image processing methods to categorize lung cancer in CT images will be more consistent and precise. This paper presents a lung cancer detection system using the Artificial Intelligence (AI) method. The study uses Median, Gaussian, and Watershed segments to reduce noisy and shredded CT images. Then, the Weight Optimization Neural Network method was used to improve accuracy and reduce the computational time. The results were compared with previous works and shows higher accuracy and shorter computational time.

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An Early Prediction of Lung Cancer using CT Scan Images

Cited by 20 publications

References 12 publications

Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

Identifying Lung Cancer Using CT Scan Images Based on Artificial Intelligence

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