Reducing False Positives for Lung Nodule Detection in Chest X-rays using Cascading CNN

Liu, Cheng‐Lin; Wang, Binquan; Jiao, Qinglei; Zhu, Ming

doi:10.1109/iciea.2019.8833699

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…A complete examination of numerous LC detection approaches for forecasting cancer is an uncontrolled development in the lung region, notably in lung X-ray images described in this literature review. Liu et al [16] developed a Convolutional Neural Network (CNN)-based cascade technique for lesion classification. To get the localization of the focal liver lesions, initially utilize the transfer learning (TL) method to learn the automatic recognition network on the experimental dataset.…”

Section: Related Workmentioning

confidence: 99%

Deep Separable Convolution Network for Prediction of Lung Diseases from X-rays

2022

IJACSA

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Accurate diagnosis of lung cancer has been critical, and image segmentation and deep learning (DL) techniques have made it easier for medical people. Yet, the concept's effectiveness is extremely limited due to a scarcity of skilled radiologists. Although emerging DL-based methods frequently necessitate accordance with the regulation, such as labelled feature map, to train such networks, which is difficult to terminate on a big scale. This study proposed a swarm intelligence based modified DL model called MSCOA-DSCN to classify and forecast various Lung Diseases through anterior X-rays. Image enhancement with a modified median filter and edge enhancement with statistical range applied for better image production. The disparity between min and max pixels focused on the Statistical range from each 3×3 input image cluster. Utilized Enriched Auto-Seed Fuzzy Means Morphological Clustering for segmentation (EASFMC); they could function together to identify edges in X-Ray imaging. Used A deep separable convolution network (DSCN) was in the created system to predict the class of lung cancer, and Modified Butterfly Optimization Algorithm (MBOA) applied for the feature selection procedure. This present study compared with various state-of-the-art classification algorithms using the NIH Chest-Xray-14 database.

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Section: Related Workmentioning

confidence: 99%

Deep Separable Convolution Network for Prediction of Lung Diseases from X-rays

2022

IJACSA

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“…Deep Mitosis (DM) technique, which consists of segmentation task, detection task, and classification task [21], always fails to differentiate between mitosis and nonmitosis cell shape. Recently, the object detection and instance segmentation framework Feature Pyramid Network (FPN) have been used to tackle different kinds of medical problems, such as detecting the porosity and cracks in concrete CT images [26], automatic segmentation of cervical nuclei [27], thyroid nodule detection from medical ultrasound images [28], detection of teeth and their components in X-ray images [29], lung nodule detection in chest X-rays images [30], and having attained outstanding results. e focal loss (FL) has been used to solve the data imbalance problem in the various kinds of biomedical datasets and performed well for minority class, such as classification of red blood cells morphology [31], colon gland instance segmentation [32], and localization of cell organelles [33].…”

Section: Introductionmentioning

confidence: 99%

Representation of Differential Learning Method for Mitosis Detection

Ali

Retta

et al. 2021

Journal of Healthcare Engineering

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The breast cancer microscopy images acquire information about the patient’s ailment, and the automated mitotic cell detection outcomes have generally been utilized to ease the massive amount of pathologist’s work and help the pathologists make clinical decisions quickly. Several previous methods were introduced to solve automated mitotic cell count problems. However, they failed to differentiate between mitotic and nonmitotic cells and come up with an imbalance problem, which affects the performance. This paper proposes a Representation Differential Learning Method (RDLM) for mitosis detection through deep learning to detect the accurate mitotic cell area on pathological images. Our proposed method has been divided into two parts: Global bank Feature Pyramid Network (GLB-FPN) and focal loss (FL). The GLB feature fusion method with FPN essentially makes the encoder-decoder pay attention, to further extract the region of interest (ROIs) for mitotic cells. On this basis, we extend the GLB-FPN with a focal loss to mitigate the data imbalance problem during the training stage. Extensive experiments have shown that RDLM significantly outperforms on visualization view and achieves the best performance in quantitative matrices than other proposed approaches on the MITOS-ATYPIA-14 contest dataset. Our framework reaches a 0.692 F1-score. Additionally, RDLM achieves 5% improvements than GLB with FPN in F1-score on the mitosis detection task.

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Pulmonary Nodule Detection and False Acceptance Reduction: Review

Pawar¹,

Patil²

2020

Advances in Intelligent Systems and Computing

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Reducing False Positives for Lung Nodule Detection in Chest X-rays using Cascading CNN

Cited by 9 publications

References 11 publications

Deep Separable Convolution Network for Prediction of Lung Diseases from X-rays

Deep Separable Convolution Network for Prediction of Lung Diseases from X-rays

Representation of Differential Learning Method for Mitosis Detection

Pulmonary Nodule Detection and False Acceptance Reduction: Review

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