A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Wang, Bin; Si, Shuaizong; Cui, Enuo; Zhang, Hai; Yang, Dongxiang; Dou, Shengchang; Zhu, Jian

doi:10.1109/access.2020.2976575

Cited by 12 publications

(2 citation statements)

References 46 publications

(78 reference statements)

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“…In recent decades, novel methods for contrast enhancement based on morphology operations have emerged [21]. Due to its capacity to extract dark and bright information from images using structural pieces of various sizes and shapes, the upper transformation has gained a lot of attention [22]. When preprocessing photos containing melanocytic lesions for a feature extraction investigation, the upper transformation was utilised to adjust the lighting.…”

Section: Figure 2 Lesions In Dermoscopy Imagesmentioning

confidence: 99%

A Multi Range Morphological Model on Dermoscopy Images with Edge Based Segmentation for Image Quality Enhancement for Skin Lesion Classification

Rapeti¹,

Reddy²

2023

RIA

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The most frequent form of cancer around the world is skin cancer. When compared to unaided visual inspection, Dermoscopy image processing enhances diagnostic accuracy for detecting malignant melanoma and other pigmented skin lesions. Medical professionals are very interested in computer-based methods that assist in diagnosis. For the most deadly skin diseases, such as melanoma, there has been a lot of research and development into the best ways to detect them. Skin cancer that originates in the pigment-producing cells known as melanocytes, melanoma, is the most severe form of the disease. Melanoma evolution might include changes in size, form, colour, irritation, and even skin disintegration. There is a need for a novel strategy to enhance the contrast and fine details of Dermoscopy Images. This study proposes a multi-range morphological approach to reducing the detrimental impact of low contrast on image quality. The rise in the number of skin cancer cases prompted the creation of these new technologies. The images must be precisely segmented in order to characterize skin lesions. That information can be used by a classifier or a dermatologist to classify a lesion more accurately, making it easier for them to do so. Segmentation issues might arise when images are obtained in an unsystematic and uncontrolled manner. Dermoscopy skin lesion photos can be used to aid in the computeraided diagnosis of melanoma by automatically selecting a lesion border. Segmenting skin lesions is a challenge because of the wide range of photography techniques used in Dermoscopy Images. The proposed model introduced a Multi Range Morphological Model on Dermoscopy Images with Edge based Segmentation (MRMM-DI-EbS) for image quality enhancement for lesion classification. The proposed model is compared with the traditional model and the results show that the proposed model performance is high in image quality enhancement.

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Section: Figure 2 Lesions In Dermoscopy Imagesmentioning

confidence: 99%

A Multi Range Morphological Model on Dermoscopy Images with Edge Based Segmentation for Image Quality Enhancement for Skin Lesion Classification

Rapeti¹,

Reddy²

2023

RIA

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“…Receptacle WANG et.al, [8] A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules. In this investigation, they present a quick and proficient CAD framework to improve the exhibition of knobs danger level characterization.…”

Section: Study Areamentioning

confidence: 99%

Lung CT Image Recognition using Deep Learning Techniques to Detect Lung Cancer

Chalasani¹

2020

IJETER

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Now a days, The digital image recognition techniques are extensively used in a couple of therapeutic domains for image improvement in earlier division and treatment stages, where the time factor is basic to discover the variety from the standard issues in target pictures, especially in various threatening development tumors, for instance, and lung infection. Lung cancer diagnosis is the most important early-stage research field for researcher. The proposed system is designed for the premature detection of lung cancer in two stages. The proposed method consists of several steps such as image acquisition, preprocessing, trivialization, thresholding, segmentation, extraction of features, and detection of neural networks. At first, the lung CT images they're input into the system and then passed through the preprocessing phase of the image using some processing techniques. Lung cancer growth is the most significant sickness cause high death rate. Also, computer vision methods helped determination can be valuable for doctors to precisely recognize the malignant growth cells. Numerous computer vision supported strategies have been contemplated and applied utilizing image recognition and deep learning. To minimize their effect in classification, the JSRT data set is considered to be the most commonly used reference data set to perform experiments. The right segmentation of the lung tumor from X-rays, CT-scans or MRI are the steps towards an integrated diagnostic device for the detection of lung cancer To train this neural network using volumes with tumor size and position, our detection is. In recent techniques, such as machine learning or deep learning, lung cancer can be predicted but this technique is not appropriate for predicting image segmentation in that particular field.

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Computer-Aided Detection and Diagnosis of Lung Nodules Using CT Scan Images: An Analytical Review

Ali

Yadav

2021

Proceedings of Second Doctoral Symposium on Computational Intelligence

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A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Cited by 12 publications

References 46 publications

A Multi Range Morphological Model on Dermoscopy Images with Edge Based Segmentation for Image Quality Enhancement for Skin Lesion Classification

A Multi Range Morphological Model on Dermoscopy Images with Edge Based Segmentation for Image Quality Enhancement for Skin Lesion Classification

Lung CT Image Recognition using Deep Learning Techniques to Detect Lung Cancer

Computer-Aided Detection and Diagnosis of Lung Nodules Using CT Scan Images: An Analytical Review

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