Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests

Oraibi, Zakariya A.; Yousif, Hayder; Hafiane, Adel; Seetharaman, Guna; Palaniappan, Kannappan

doi:10.1109/icip.2018.8451287

Cited by 14 publications

(13 citation statements)

References 12 publications

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“…In this section, we demonstrate the performance of our approach for classifying 12 CNT classes, defined later. We perform classification using 1000 trees RF classifier, which previously proved to be very efficient [41] [39]. During training and testing, we used 5 fold cross-validation, where 80% of data was used for training and the remaining 20% was used for testing.…”

Section: Resultsmentioning

confidence: 99%

“…RF classifier with 1000 trees was tested before to classify Human Epithelial type 2 (HEp-2) images and it was very successful in recognizing six and seven classes of cells and specimen samples respectively [39] [41]. Hence, the RF classifier was selected to classify CNT forest images that yields impressive results as shown in Table II, when adopting the RF with many trees to aide the voting process within the classifier.…”

Section: Resultsmentioning

confidence: 99%

“…Local feature extractors, individually and in combination, followed by random forest classifier were employed to create the class predictor model. We used four local descriptors that are derived from both Local Binary Pattern (LBP) and Motif Cooccurrence Matrix (MCM) descriptors [39][40] [41]. The descriptors are Rotation Invariance Cooccurrence among Local Binary Patterns (RIC-LBP), Joint Adaptive Median Binary Patterns (JAMBP), Joint Motif Labels (JML), and Motif Patterns (MP) encoded with RIC-LBP [40].…”

Section: Introductionmentioning

confidence: 99%

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Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Hajilounezhad¹,

Oraibi²,

Surya³

et al. 2020

Preprint

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The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Hajilounezhad¹,

Oraibi²,

Surya³

et al. 2020

Preprint

Self Cite

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“…Oraibi et al [26] used the known CNN VGG-19 to extract features and combine them with local features such as RIC-LBP (rotation invariant co-occurrence local binary pattern) and JML (joint motif labels) for an efficient cell classification. The combination of features was used to train a random forest classifier.…”

Section: Introductionmentioning

confidence: 99%

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

2019

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The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database.

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“…Local feature extractors, individually and in combination, followed by random forest classifier were employed to create the class predictor model. We used four local descriptors that are derived from both Local Binary Pattern (LBP) and Motif Cooccurrence Matrix (MCM) descriptors [150][151][152] The reason behind using multiple local descriptors is to capture more texture features and to ultimately improve the final classification accuracy.…”

Section: Exploration Of Carbon Nanotube Forest Synthesis-structure Relationships Using Physics-based Simulation and Machine Learning (Hanmentioning

confidence: 99%

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

Hajilounezhad¹

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This work is aimed to explore process-structure-property relationships of carbon nanotube (CNT) forests. CNTs have superior mechanical, electrical and thermal properties that make them suitable for many applications. Yet, due to lack of manufacturing control, there is a huge performance gap between promising properties of individual CNTs and CNT forest properties that hinders their adoption into potential industrial applications. In this research, computational modelling, in-situ electron microscopy for CNT synthesis, and data-driven and high-throughput deep convolutional neural networks are employed to not only accelerate implementing CNTs in various applications but also to establish a framework to make validated predictive models that can be easily extended to achieve application-tailored synthesis of any materials. A time-resolved and physics-based finite-element simulation tool is modelled in MATLAB to investigate synthesis of CNT forests, specially to study the CNT-CNT interactions and generated mechanical forces and their role in ensemble structure and properties. A companion numerical model with similar construct is then employed to examine forest mechanical properties in compression. In addition, in-situ experiments are carried out inside Environmental Scanning Electron Microscope (ESEM) to nucleate and synthesize CNTs. Findings may primarily be used to expand the forest growth and self-assembly knowledge and to validate the assumptions of simulation package. Also, SEM images can be used as feed database to construct a deep learning model to grow CNTs by design. The chemical vapor deposition parameter space of CNT synthesis is so vast that it is not possible to investigate all conceivable combinations in terms of time and costs. Hence, simulated CNT forest morphology images are used to train machine learning and learning algorithms that are able to predict CNT synthesis conditions based on desired properties. Exceptionally high prediction accuracies of R2 > 0.94 is achieved for buckling load and stiffness, as well as accuracies of > 0.91 for the classification task. This high classification accuracy promotes discovering the CNT forest synthesis-structure relationships so that their promising performance can be adopted in real world applications. We foresee this work as a meaningful step towards creating an unsupervised simulation using machine learning techniques that can seek out the desired CNT forest synthesis parameters to achieve desired property sets for diverse applications.

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Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests

Cited by 14 publications

References 12 publications

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

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