ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis

Suresh, Supriya; Mohan, Subaji

doi:10.1007/s00521-020-04787-w

Cited by 65 publications

(22 citation statements)

References 48 publications

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“…S. Suresh and S. Mohan [22] proposed an eight-layer CNN architecture for categorizing CT images of lung lesions into one of three groups. Segmentation was applied on images to extract nodule regions of interest in collaboration with experts.…”

Section: Related Workmentioning

confidence: 99%

“… Worked on a smaller number of classes or a less diverse dataset [20,21,25,26,27].  Reported low accuracy [11,12,14,15,20,22] The major contributions of this paper are outlined below: • Most earlier cancer detection studies focused on a single form of cancer, however in this study, we used our model to identify lung and colon cancer at the same time.…”

Section: Limitations Of the Related Workmentioning

confidence: 99%

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Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing

et al. 2022

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Cancer accounts for a huge mortality rate due to its aggressiveness, colossal potential of metastasis, and heterogeneity (causing resistance against chemotherapy). Lung and colon cancers are among the most prevalent types of cancer around the globe that can occur in both males and females. Early and accurate diagnosis of these cancers can substantially improve the quality of treatment as well as the survival rate of cancer patients. We propose a highly accurate and computationally efficient model for the swift and accurate diagnosis of lung and colon cancers as an alternative to current cancer detection methods. In this study, a large dataset of lung and colon histopathology images was employed for training and the validation process. The dataset is comprised of 25000 histopathology images of lung and colon tissues equally divided into 5 classes. A pretrained neural network (AlexNet) was tuned by modifying the four of its layers before training it on the dataset. Initial classification results were promising for all classes of images except for one class with an overall accuracy of 89%. To improve the overall accuracy and keep the model computationally efficient, instead of implementing image enhancement techniques on the entire dataset, the quality of images of the underperforming class was improved by applying a contrast enhancement technique which is fairly simple and efficient. The implementation of the proposed methodology has not only improved the overall accuracy from 89% to 98.4% but has also proved computationally efficient.

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Section: Related Workmentioning

confidence: 99%

Section: Limitations Of the Related Workmentioning

confidence: 99%

Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing

et al. 2022

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“…They collected CT scan images from the Lung Image Database Consortium (LIDC) and Infectious Disease Research Institute (IDRI) databases and employed Generative Adversarial Networks (GANs) to generate additional images to increase the sample size. They achieved a 93.9% classification accuracy (maximum) using CNN-based classification algorithms [ 33 ]. Masud et al described a pulmonary nodule detection method based on CT scan images using a light CNN architecture [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud

Sikder

Nahid

et al. 2021

Sensors

234

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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

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“…This approach has minimum misclassification error, although computational difficulty is high. Suresh and Mohan [ 15 ] presented CNN architecture for detecting lung cancer. In this technique, preprocessing was carried out to eliminate noise and desired nodule region is extracted.…”

Section: Literature Reviewmentioning

confidence: 99%

Water Cycle Bat Algorithm and Dictionary-Based Deformable Model for Lung Tumor Segmentation

Shetty

Jayadevappa

Veena

2021

International Journal of Biomedical Imaging

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Among the different types of cancers, lung cancer is one of the widespread diseases which causes the highest number of deaths every year. The early detection of lung cancer is very essential for increasing the survival rate in patients. Although computed tomography (CT) is the preferred choice for lungs imaging, sometimes CT images may produce less tumor visibility regions and unconstructive rates in tumor portions. Hence, the development of an efficient segmentation technique is necessary. In this paper, water cycle bat algorithm- (WCBA-) based deformable model approach is proposed for lung tumor segmentation. In the preprocessing stage, a median filter is used to remove the noise from the input image and to segment the lung lobe regions, and Bayesian fuzzy clustering is applied. In the proposed method, deformable model is modified by the dictionary-based algorithm to segment the lung tumor accurately. In the dictionary-based algorithm, the update equation is modified by the proposed WCBA and is designed by integrating water cycle algorithm (WCA) and bat algorithm (BA).

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ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis

Cited by 65 publications

References 48 publications

Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing

Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Water Cycle Bat Algorithm and Dictionary-Based Deformable Model for Lung Tumor Segmentation

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