Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Haryanto, Toto; Pratama, Adib; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Pidanič, Jan

doi:10.3844/jcssp.2020.280.294

Cited by 20 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Finally, we 3D scanned each actuated sample using a laser scanner. orientation from the reference pixel (Haryanto et al, 2020).…”

Section: Sample Production and Data Acquisitionmentioning

confidence: 99%

A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning

Akbar¹,

Wood²,

Kiesewetter³

et al. 2022

CAADRIA Proceedings

View full text Add to dashboard Cite

This paper develops a workflow to train machine learning (ML) models with a small dataset from physical samples to predict the curvatures of self-shaping wood bilayers based on local variations in the grain. In contrast to state-of-the-art predictive models, specifically 1.) a 2D Timoshenko model and 2.) a 3D numerical model with a rheological model, our method accounts for natural and unavoidable material variations. In this paper, we only focus on local grain variations as the main driver for curvatures in small-scale material samples. We extracted a feature matrix from grain images of active and passive layers as a Grey Level Co-Occurrence Matrix and used it as the input for our ML models. We also analysed the impact of grain variations on the feature matrix. We trained and tested several tree-based regression models with different features. The models achieved very accurate predictions for curvatures in each sample (R²>0.9) and extend the range of parameters that is incalculable by a Timoshenko model. This research contributes to the material-efficient design of weatherresponsive shape-changing wood structures by further leveraging the use of natural material features and explainable data-driven modelling and extends the topic in ML for material behaviour-driven design among the CAADRIA community.

show abstract

“…Finally, we 3D scanned each actuated sample using a laser scanner. orientation from the reference pixel (Haryanto et al, 2020).…”

Section: Sample Production and Data Acquisitionmentioning

confidence: 99%

A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning

Akbar¹,

Wood²,

Kiesewetter³

et al. 2022

CAADRIA Proceedings

View full text Add to dashboard Cite

show abstract

“…Finally, we 3D scanned each actuated sample using a laser scanner. (0, 45, 90, and 135) and distance (d = 2) orientation from the reference pixel (Haryanto et al, 2020).…”

Section: Sample Production and Data Acquisitionmentioning

confidence: 99%

“…Figure 2. Illustration of GLCM with various angles(0, 45, 90, and 135) and distance (d = 2) orientation from the reference pixel(Haryanto et al, 2020).…”

mentioning

confidence: 99%

Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1]

2022

CAADRIA Proceedings

View full text Add to dashboard Cite

The work collected in these volumes has been produced during extraordinary circumstances, as the Covid-19 pandemic has continued to impact working and living conditions. Papers have been written, reviewed and edited throughout lockdowns, challenging family situations and health crises. The high number of submissions and quality of the papers is a testament to the resilience and strength of the CAADRIA community.The papers in this publication have been selected through a two-stage, doubleblind peer review process. All reviews and papers have been evaluated by the Paper Selection Committee. After receiving an initial 488 abstract submissions, a final set of 150 full papers has been selected for publication. We thank all authors for their contributions to the process and congratulate the accepted paper authors.The papers in these proceedings are specifically selected for their contribution to this year's conference theme, following the conference organisers' call for authors to position their work in relation to the United Nations Sustainable Development Goals (SDGs). As the world is experiencing the increasing impacts of climate change, there is an urgent need to reflect on the potential of the latest research in architecture, urbanism and construction to address these global challenges.Our special thanks go out to our more than 180 international reviewers, who volunteered their valuable time and effort. We also thank the organising team in Sydney for developing the 2022 CAADRIA Conference as a strong collaborative effort between academic institutions, industry and practice. We thank Gabriel Wurzer for his support during the proceedings' final production stage, and for facilitating their publication via the open access database CumInCAD.org. Finally, we sincerely thank the CAADRIA community for offering us the honour to serve as members of the paper selection committee for Post-Carbon, the 27 th International CAADRIA Conference 2022.

show abstract

“…Selain itu, Pada penelitian dengan topik klasifikasi kanker payudara menggunakan ekstraksi fitur GLCM (Grey Level Co-Occurance Matrix) didapatkan akurasi dalam mendeteksi kanker sebesar 80% [5]. Diperkuat dengan penelitian klasifikasi citra hispatologi kolon menggunakan ekstraksi fitur GLCM didapatkan akurasi yang cukup besar dalam mengklasifikasikan suatu citra [6].…”

Section: Pendahuluanunclassified

Untitled

Saraswati¹,

Priharti²,

Alia³

2022

tektrika

View full text Add to dashboard Cite

Melanoma merupakan salah satu jenis penyakit kulit berupa tumor ganas yang berkembang pada sel melanosit penghasil melanin. Penyakit ini merupakan penyakit langka, namun memiliki risiko kematian yang tinggi jika tidak segera ditangani. Salah satu cara yang dapat membantu penanganan melanoma adalah diagnosis secara dini terhadap penyakit tersebut. Pada penelitian ini dirancang sebuah pendeteksi penyakit melanoma menggunakan Raspberry pi dan pi camera dengan mengimplementasikan metode ekstraksi fitur Grey Level Co-Occurance Matrix (GLCM) dan metode klasifikasi Convolutional Neural Network (CNN). Citra input akan diolah melalui proses preprocessing berupa proses grayscale, image adjustment, dan cropping. Citra hasil pre-processing akan diekstraksi menggunakan metode GLCM berdasarkan parameter contrast, correlation, entropy, dan uniformity. Tahap terakhir, citra akan diklasifikasikan apakah merupakan penyakit melanoma atau non-melanoma menggunakan metode CNN. Pengujian dilakukan menggunakan dataset yang berjumlah 416 data citra nevus dan melanoma serta citra validasi yang berjumlah 16 sampel citra nevus. Dari hasil pengujian didapatkan hasil akurasi sebesar 85% dengan F1-score sebesar 89% sehingga hasil penelitian ini dapat digunakan sebagai salah satu alat bantu untuk proses screening kasus penyakit melanoma.

show abstract

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Cited by 20 publications

References 23 publications

A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning

A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning

Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1]

Untitled

Contact Info

Product

Resources

About