Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review

Raji, Hassan; Tayyab, Muhammad; Sui, Jianye; Mahmoodi, Seyed Reza; Javanmard, Mehdi

doi:10.1007/s10544-022-00627-x

Cited by 32 publications

(15 citation statements)

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“…Specifically, in the field of biophotonics, machine learning models using SERS can be efficiently classified into three domains: identification, classification, and quantification, with interests such as disease and molecular diagnosis [ 367 , 368 ]; microorganism classification, identification, etc. [ 369 , 370 , 371 , 372 ]; and cancer diagnosis [ 373 ], as shown in Figure 7 . In addition, machine learning was also used to improve data collection to overcome signal fluctuations and enhance the usability on site [ 374 ], to estimate the effect of scattering [ 375 ] and for the SERS signal enhancement itself [ 376 ].…”

Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

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Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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“…The author of [51] proposed another approach to identify acute lymphoblastic leukemia using SVM classifiers. In [52], the author uses Kmeans clustering and support vector machines to recognize acute lymphoblastic leukemia cells in microscopic images [53]- [55]. Recent literature has applied deep learning-based techniques to categorize ALLs with significant results.…”

Section: Pretraind Vgg16mentioning

confidence: 99%

Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Batool

Byun

2023

IEEE Access

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Acute lymphoblastic leukemia (ALL) occurs when undeveloped lymphocytes are grown excessively in the bone marrow due to the formation of many immature white blood cells (WBC) in the bone marrow to destroy the healthy cell, which may lead to death. White blood cells are part of the immune system fighting against germs. A timely and accurate cancer diagnosis is important for effective treatment to improve survival rates. Since the image of acute lymphoblastic leukemia cells (cancer cells) under the microscope is complicated to recognize the difference between ALL cancer cells and normal cells. In order to reduce the severity of this disease, it is necessary to classify immature cells at an early stage. In recent years, different classification models have been introduced based on machine learning (ML) and deep learning (DL) algorithms, but they need to be improved. This work enhances the diagnosis of ALL with a computer-aided system that yields accurate results by using deep learning techniques. This research proposed a lightweight DL-assisted robust model based on EfficientNet-B3 using depthwise separable convolutions for classifying acute lymphoblastic leukemia and normal cells in the white blood cell images dataset. The main objective of the proposed lightweight EfficientNet-B3 is to utilize less trainable parameters to enhance the performance and efficiency of the classification of leukemia. Furthermore, two publicly available datasets are considered to evaluate the effectiveness and generalization of the proposed lightweight EfficientNet-B3. In addition, different measures are employed to evaluate the effectiveness and efficiency of the proposed classifier for the accurate and reliable classification of leukaemia cells. In addition, a detailed analysis is given to evaluate and compare the performance and efficiency of the proposed with existing pre-trained and ensemble DL classifiers. Based on experimental results, it is investigated that the proposed model for image classification achieves better performance and outperforms the existing benchmark DL and other ensemble classifiers. Moreover, our finding suggests that the proposed lightweight EfficientNet-B3 model is reliable and generalized to facilitate clinical research and practitioners for leukemia detection. INDEX TERMS Acute lymphoblastic leukemia (ALL), EfficientNet-B3, CNN, White blood cell image classification, Deep Learning I. INTRODUCTIONLeukemia is a type of blood cancer that affect the white blood cells to become cancerous. The immune system of the body is facing the risk of these abnormal blood cells, which affect the bone marrow and white and red blood cells. Bone marrow cancer affects children and teenagers. Acute leukemia has two types: acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Lymphocytes,

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“…Different machine learning algorithms have been used in most of the segmentation techniques. The purpose of cell segmentation is to identify the boundary between the nucleus and cytoplasm for further specification, such as characterizing the characteristics of the nucleus, the characteristics of the cytoplasm, and the nuclear-to-cytoplasmic ratio, which is useful for identification [3].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Classification of Heterogeneous Blood Cells Using Deep Learning and contourlet Transform

Eslamifar,

Soltani,

Fatemi

2024

Preprint

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The classification of human blood cells is very important in the diagnosis of inflammation, infection and blood disorders such as leukemia. Diagnosis of blood malignancies requires identification and classification of white blood cells in peripheral blood smear. The pathologist spends a lot of time analyzing blood cells to determine the minor differences between blood subsets. Due to the high similarity of blood types, human error is sometimes possible. Manual procedures for diagnosing blood diseases are time-consuming, subjective and prone to human error. Therefore, we need machine analysis of microscopic images. Usually, methods based on image processing contain limitations. On the other hand, with the increase in computational processing power in computer-based clinical diagnosis systems, it has enabled the use of machine learning methods. In this article, we will use the combination of deep learning; Gabor filter and wavelet transform to provide a high accuracy blood cells classification model while extracting features from macroscopic images. The basis of the current research is the classification of blood smear images using the combination of contourlet transform, recurrent neural network and optimization method. Feature extraction is based on the combination of wavelet transform and recurrent neural network and feature selection is based on the African vulture optimization method. Finally, an innovative classifier based on clustering is presented to classify different blood cells. Based on the results obtained on the set of Jiangxi Tecom images, the proposed design has achieved an acceptable accuracy and has been able to increase the precision, recall and F-Measure.

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Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review

Cited by 32 publications

References 100 publications

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Multiclass Classification of Heterogeneous Blood Cells Using Deep Learning and contourlet Transform

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