Label-Free Normal and Cancer Cells Classification Combining Prony’s Method and Optical Techniques

Abdulgani, Aysha F.; Ahmad, Mahmoud Al

doi:10.1109/access.2020.2973468

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…Label-free approaches, according to Abdulgani and Al Ahmad, do not induce cell harm or lead to any changes in cell composition or inherent traits. This research used advances in optical measurements with Prony approaches to improve cell classification using measured optical profiles [ 22 ]. By the improvement of Tobacco Exposure Pattern (TEP) classification models and uncovering their interaction linkages at multiple biological levels, He et al were able to identify signature genes.…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Kasinathan¹,

Jayakumar²

2022

BioMed Research International

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Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.

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Section: Related Workmentioning

confidence: 99%

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Kasinathan¹,

Jayakumar²

2022

BioMed Research International

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“…Visual liver issues detection is identifying the abnormality on liver if any and then through pattern matching concluding the type of issues like accidental scar, fungus, tissue damage, or the cancer. This result is based in the knowledge and expertise about patterns [2].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Cancer Symptoms Detection from Liver CT Images Using Multistage Pre-Processors

Siddique

Singh

Hasan³

2023

IJEER

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Visually cancer is the abnormal pattern with predefined structure could be found in liver Computed Tomography (CT) images. Using deep convolution neural network computation and image processing, this detected abnormal pattern cluster can be classified in different liver issue types. Full size liver CT scan images consisting different body parts, and these are ultrasonic based gray scaled image construction. The primary challenge in the cancer symptoms detection process is to extract the liver area out of image then finding out the actual area of abnormality to conclude whether abnormality is cancer or any other issues on liver. This is two stage processes, first is to segment the abnormality area and second is to perform pattern matching to identify the abnormality. This research paper primarily focuses on different pre-processing techniques and stages involved in liver abnormality segmentation.

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“…The proposed framework has shown a high accuracy of 97.22% for liver volume measurements and a 0.92 average dice coefficient. Abdulgani and Ahmad [37] anticipated an unsupervised method for cancer detection. Here classification using optical measurement is utilized that interact differently with light as various cells has distinct composition.…”

Section: Introductionmentioning

confidence: 99%

Automatic Liver Cancer Detection Using Deep Convolution Neural Network

Napte,

Mahajan,

Urooj

2023

IEEE Access

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Automatic liver cancer detection (ALCD) is very crucial in automatic biomedical image analysis diagnosis as it is the largest organ in the body and plays a significant role in the metabolic process as well as the elimination of toxins. In the last decade, various machine and deep learning schemes have been investigated for automatic ALCD using computed tomography (CT) images. However, ALCD in CT images is challenging because of the noise, intricate structure of abdominal computed tomography (CT) images, and textural changes throughout the CT images making liver segmentation a vital challenge that may result in both under-segmentation (u-seg) and over-segmentation (o-seg) of the organ. This paper presents liver segmentation based on the proposed Edge Strengthening Parallel UNet (ESP-UNet) for liver segmentation to avoid the u-seg and o-seg of the liver in CT images. Further, it offered ALCD based on lightweight sequential Deep Convolution Neural Networks (DCNN). The consequences of ESP-UNet DCNN-based ALCD are evaluated based on accuracy, recall, precision, and F1-score. The suggested approach provides a noteworthy improvement in ALCD over the traditional state of arts.

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Label-Free Normal and Cancer Cells Classification Combining Prony’s Method and Optical Techniques

Cited by 11 publications

References 27 publications

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Cancer Symptoms Detection from Liver CT Images Using Multistage Pre-Processors

Automatic Liver Cancer Detection Using Deep Convolution Neural Network

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