A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification

Irawati, Indrarini Dyah; Hadiyoso, Sugondo; Budiman, Gelar; Fahmi, Arfianto; Latip, Rohaya

doi:10.4103/jmss.jmss_127_21

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…The advent of digital image processing techniques has significantly transformed various sectors, notably in healthcare [4,5]. These techniques have been particularly useful in early-stage detection and classification of diseases or cancers via blood cell image analysis, as reported in several studies [6][7][8][9][10][11][12]. In one such study, the authors employed colorbased k-means technique for lymphocyte extraction from segmented images for the detection and classification of ALL [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Detection of Acute Lymphoblastic Leukemia Using Deep Transfer Learning Model

Fata,

Irawati,

Hadiyoso

2023

RIA

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Acute Lymphoblastic Leukemia (ALL), a rapidly progressing malignancy originating from hematopoietic cells, necessitates prompt and precise diagnosis due to its potential lethality within a short span of months. Technological advancements are therefore pivotal in aiding medical practitioners to reduce the probability of human error, expedite diagnosis and subsequently, improve patient outcomes. This study presents a novel system leveraging Convolutional Neural Networks (CNNs), capable of diagnosing ALL through image analysis of affected cells. Our proposed system employs two well-established CNN architectures, VGG16 and ResNet50, coupled with two optimization algorithms, Adam and RMSprop, to classify image data into two distinct categories. The utilized dataset, C-NMC Leukemia 2019, was subjected to a variety of test scenarios involving differing epoch variations (10, 20, 30, 40, 50, 60, 80, and 100) and a consistent learning rate of 0.0001. The results suggest that the proposed system exhibits superior performance when utilizing the VGG16 architecture in conjunction with the Adam optimizer, achieving a training accuracy of 93.80% and a testing accuracy of 87.00%. The findings of this study accentuate the potential of integrating deep learning techniques into the diagnostic process of ALL, thereby facilitating rapid, precise detection and ultimately contributing to the improvement of patient prognosis.

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Section: Introductionmentioning

confidence: 99%

Optimal Detection of Acute Lymphoblastic Leukemia Using Deep Transfer Learning Model

Fata,

Irawati,

Hadiyoso

2023

RIA

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Machine Learning Model to Determine Dominant Features in Palm Kernel Cake Quality

Irfansyah,

Purwanto,

Wijaya

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Palm kernel cake (PKC), a by-product of palm oil production, has the potential to serve as a cost-effective alternative to traditional feed ingredients such as soybeans, owing to its nutritional value and affordability. However, its quality can vary significantly, impacting its suitability for different livestock. This study aims to develop a machine learning model to identify the dominant features influencing PKC quality using data from proximate analysis. Our model utilizes feature selection techniques and machine learning algorithms with a decision tree to determine the critical factors affecting key quality parameters, such as fat, dry matter, crude protein, crude fiber, and ash. We explore the relationship between these features and PKC suitability for different animal diets. Our novel approach sheds light on the intricate factors governing PKC quality, enabling improved processing and utilization of this valuable resource.

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Block-based compressive sensing in deep learning using AlexNet for vegetable classification

Dyah Irawati,

Budiman,

Saidah

et al. 2023

PeerJ Computer Science

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Vegetables can be distinguished according to differences in color, shape, and texture. The deep learning convolutional neural network (CNN) method is a technique that can be used to classify types of vegetables for various applications in agriculture. This study proposes a vegetable classification technique that uses the CNN AlexNet model and applies compressive sensing (CS) to reduce computing time and save storage space. In CS, discrete cosine transform (DCT) is applied for the sparsing process, Gaussian distribution for sampling, and orthogonal matching pursuit (OMP) for reconstruction. Simulation results on 600 images for four types of vegetables showed a maximum test accuracy of 98% for the AlexNet method, while the combined block-based CS using the AlexNet method produced a maximum accuracy of 96.66% with a compression ratio of 2×. Our results indicated that AlexNet CNN architecture and block-based CS in AlexNet can classify vegetable images better than previous methods.

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A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification

Cited by 3 publications

References 29 publications

Optimal Detection of Acute Lymphoblastic Leukemia Using Deep Transfer Learning Model

Optimal Detection of Acute Lymphoblastic Leukemia Using Deep Transfer Learning Model

Machine Learning Model to Determine Dominant Features in Palm Kernel Cake Quality

Block-based compressive sensing in deep learning using AlexNet for vegetable classification

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