Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

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New microRNA-based diagnostic test for lung cancer classification.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.10528 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 10528-10528

Author(s):

Ranit Aharonov ◽

Gila Lithwick Yanai ◽

Hila Benjamin ◽

Mats Olot Sanden ◽

Marluce Bibbo ◽

...

Keyword(s):

Lung Cancer ◽

Cell Carcinoma ◽

Expression Profiles ◽

Lung Tumors ◽

Small Cell ◽

Diagnostic Methods ◽

Expression Levels ◽

Qrt Pcr ◽

Cancer Types

10528 Background: Lung cancer is the leading cause of cancer deaths in the US. Treatment options are determined by tumor subtyping, for which there is lack of standardized, objective, and highly accurate techniques. In 20%-30% of cases significant limitations of tumor quantity and quality prevent full classification of the tumor using traditional diagnostic methods. Using microRNA microarray data generated from over a hundred formalin-fixed, paraffin-embedded (FFPE) primary lung cancer samples, we have identified microRNA expression profiles that differ significantly for the main lung cancer types. Based on these findings, we have developed and validated a microRNA-based qRT-PCR assay that differentiates primary lung cancers into four types: squamous cell carcinoma, non-squamous non-small cell lung cancer, carcinoid and small cell carcinoma. Methods: Over 700 primary tumor samples from different histological types of lung cancer were collected. Samples included FFPE blocks from resection or biopsies and cell blocks from cytology specimens including fine needle aspiration, bronchial brushing and bronchial washing. High-quality RNA was extracted from the samples using proprietary protocols. Expression levels of potential microRNA biomarkers were profiled using microarrays followed by a sensitive and specific qRT-PCR platform. An assay for lung tumors classification using 8 microRNAs on qRT-PCR was developed based on data from 261 samples. This assay was validated on an independent blinded set of 451 cytological and pathological samples. Results: Using the expression levels of 8 microRNAs measured in qRT-PCR, accurate classification of the primary lung tumors into the four main cancer types is obtained. The microRNA-based assay reached an accuracy of 94%. Moreover, cytological samples composed over 50% of the validation set and reached an accuracy of 95%. Conclusions: We present here a new microRNA-based assay for the classification of the four main types of lung cancer based only on the expression of 8 microRNAs. This assay displays very high levels of accuracy for both pathological and cytological samples. The assay comprises a standardized, well-tested and objective tool which can assist physicians in the diagnosis of lung cancer.

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An Annotation-free Whole-slide Training Approach to Pathological Classification of Lung Cancer Types by Deep Neural Network

10.21203/rs.3.rs-48727/v1 ◽

2020 ◽

Author(s):

Chi-Long Chen ◽

Chi-Chung Chen ◽

Wei-Hsiang Yu ◽

Szu-Hua Chen ◽

Yu-Chan Chang ◽

...

Keyword(s):

Lung Cancer ◽

Digital Pathology ◽

Memory Consumption ◽

Cancer Dataset ◽

Lung Cancers ◽

Training Approach ◽

Cancer Types ◽

Critical Regions ◽

Whole Slide Images

Abstract Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs). Most studies adopt patch-based methods which, however, require well annotated data for training. These are typically done by laboriously free-hand contouring on the WSI by experts. To both alleviate annotation burdens of experts and enjoy benefits from scaling up amounts of data, we develop a whole-slide training method for entire WSIs to classify types of lung cancers using slide-level diagnoses. Our method leverages unified memory to offload the excessive amount of memory consumption to host memory to train a classifier by entire hundreds-of-million-pixels slides. Experiments were conducted on the lung cancer dataset which contains 9,662 digital slides with various main types. The results showed that the proposed method can achieve an AUC of 0.950 and 0.924 for adenocarcinoma and squamous cell carcinoma on a separate testing set respectively. Furthermore, critical regions highlighted by the class activation map (CAM) technique of our model reveals a high correspondence to cancerous areas annotated by pathologists.

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Concise genetic profile of lung carcinoma

Postgraduate Medical Journal ◽

10.1136/postgradmedj-2021-139860 ◽

2021 ◽

pp. postgradmedj-2021-139860

Author(s):

Srikanth Umakanthan ◽

Maryann M Bukelo

Keyword(s):

Lung Cancer ◽

Cell Carcinoma ◽

Microscopic Analysis ◽

Genetic Alterations ◽

Small Cell ◽

The Cancer Genome Atlas ◽

Genetic Profile ◽

Cancer Evolution ◽

Lung Cancers

The WHO classification of lung cancer (2015) is based on immunohistochemistry and molecular evaluation. This also includes microscopic analysis of morphological patterns that aids in the pathological diagnosis and classification of lung cancers. Lung cancers are the leading cause of cancer deaths worldwide. Recent advancements in identifying the etiopathogenesis are majorly driven by gene mutation studies. This has been explained by The Cancer Genome Atlas, next-generation sequencer and TRAcking non-small cell lung cancer evolution through therapy [Rx]. This article reviews the genetic profile of adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell neuroendocrine carcinoma and pulmonary carcinoids. This includes the prolific genetic alterations and novel molecular changes seen in these tumours. In addition, target- specific drugs that have shown promising effects in clinical use and trials are also briefly discussed.

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Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Scientific Reports ◽

10.1038/s41598-021-89365-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Song-Quan Ong ◽

Hamdan Ahmad ◽

Gomesh Nair ◽

Pradeep Isawasan ◽

Abdul Hafiz Ab Majid

Keyword(s):

Deep Learning ◽

Aedes Aegypti ◽

Real Time ◽

Aedes Albopictus ◽

Automated Classification ◽

Expert Performance ◽

Deep Convolutional Neural Networks ◽

Significant Difference ◽

Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

Bioscience Reports ◽

10.1042/bsr20180289 ◽

2018 ◽

Vol 38 (3) ◽

Cited By ~ 11

Author(s):

Miao Wu ◽

Chuanbo Yan ◽

Huiqiang Liu ◽

Qian Liu

Keyword(s):

Neural Networks ◽

Ovarian Cancer ◽

Convolutional Neural Networks ◽

Image Data ◽

Training Data ◽

Serous Carcinoma ◽

Deep Convolutional Neural Networks ◽

Ovarian Cancers ◽

Cancer Types

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.

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The Regulators Associated With N6-Methyladenosine in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma Reveal New Clinical and Prognostic Markers

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.741521 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shuzhen Tan ◽

Zesong Li ◽

Kai Li ◽

Yingqi Li ◽

Guosheng Liang ◽

...

Keyword(s):

Lung Cancer ◽

Squamous Cell Carcinoma ◽

Lung Adenocarcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Prognostic Markers ◽

Cox Regression ◽

Lung Squamous Cell Carcinoma ◽

Lung Cancers ◽

Pathological Characteristics

N6-methyladenosine (m6A) methylation is of significant importance in the initiation and progression of tumors, but how specific genes take effect in different lung cancers still needs to be explored. The aim of this study is to analyze the correlation between the m6A RNA methylation regulators and the occurrence and development of lung cancer. The data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were obtained through the TCGA database. We systematically analyzed the related pathological characteristics and prognostic factors by applying univariate and multivariate Cox regression, as well as LASSO Cox regression. Some of 23 m6A regulators are identified as having high expression in lung cancer. In addition, risk score has been shown to be an independent prognostic factor in lung cancer. Our research not only fully reveals that m6A regulators and clinical pathological characteristics are potentially useful with respect to survival and prognosis in different lung tumors but also can lay a theoretical root for the treatment for lung cancer—notably, to point out a new direction for the development of treatment.

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Automated communication system for detection of lung cancer using catastrophe features

Informatologia ◽

10.32914/i.53.3-4.5 ◽

2020 ◽

Vol 53 (3-4) ◽

pp. 184-190

Author(s):

Ramaiah Arun ◽

Shanmugasundaram Singaravelan

Keyword(s):

Lung Cancer ◽

Support Vector ◽

Color Spaces ◽

Lung Cancers ◽

Clustering Techniques ◽

Early Stages ◽

Fuzzy C Means Clustering ◽

Glandular Cells ◽

Medical Advances

One of the biggest challenges the world face today is the mortality due to Cancer. One in four of all diagnosed cancers involve the lung cancer, where the mortality rate is high, even after so much of technical and medical advances. Most lung cancer cases are diagnosed either in the third or fourth stage, when the disease is not treatable. The main reason for the highest mortality, due to lung cancer is because of non availability of prescreening system which can analyze the cancer cells at early stages. So it is necessary to develop a prescreening system which helps doctors to find and detect lung cancer at early stages. Out of all various types of lung cancers, adenocarcinoma is increasing at an alarming rate. The reason is mainly attributed to the increased rate of smoking - both active and passive. In the present work, a system for the classification of lung glandular cells for early detection of Cancer using multiple color spaces is developed. For segmentation, various clustering techniques like K-Means clustering and Fuzzy C-Means clustering on various Color spaces such as HSV, CIELAB, CIEXYy and CIELUV are used. Features are Extracted and classified using Support Vector Machine (SVM).

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The FIRST Classifier: compact and extended radio galaxy classification using deep Convolutional Neural Networks

10.31730/osf.io/9d2h3 ◽

2018 ◽

Author(s):

Wathela Alhassan

Keyword(s):

Radio Galaxies ◽

Radio Sources ◽

Automated Classification ◽

Imaging Data ◽

Deep Convolutional Neural Networks ◽

Line System ◽

New Radio ◽

Extended Radio ◽

Galaxy Classification

Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio galaxies. Different classes of radio galaxies can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these galaxies based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Radio galaxies have been traditionally classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent' morphologies arising from environmental factors or intrinsic properties. In this work we present the FIRST Classifier, an on-line system for automated classification of Compact and Extended radio sources. We developed the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of com- pact and extended radio sources observed in the FIRST radio survey. Our model achieved an overall accuracy of 97% and a recall of 98%, 100%, 98% and 93% for Compact, BENT, FRI and FRII galaxies respectively. The current version of the FIRST classifier is able to identify the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT).

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Towards the 9th Edition of the Tumour, Node and Metastasis Classification of Lung Cancer. A Historical Appraisal and Future Perspectives

Advances in Thoracic Diseases ◽

10.15342/atd.v1ir.293 ◽

2019 ◽

Vol 1 (2) ◽

Author(s):

Ramón Rami-Porta

Keyword(s):

Lung Cancer ◽

International Association ◽

Tumour Size ◽

Primary Tumour ◽

Cancer Control ◽

Regional Lymph Nodes ◽

Lung Cancers ◽

Lung Cancer Patients ◽

Pathologic Staging

Since 1966 the classification of anatomic extent of lung cancer, based on the primary tumour (T), the loco-regional lymph nodes (N) and the metastases (M) has been used in the management of lung cancer patients. Developed by Pierre Denoix, it was adopted by the Union for International Cancer Control and the American Joint Committee on Cancer. Clifton Mountain revised the second through the sixth editions based on a North American database of more than 5000 patients. For the seventh and the eighth editions, the International Association for the Study of Lung Cancer (IASLC) collected international databases of around 100,000 patients worldwide that allowed the introduction of innovations in both editions, namely the subdivision of the T and M categories based on tumour size and on the location and number of metastases, respectively. The revisions also showed the prognostic relevance of the quantification of nodal disease, and proposed recommendations on how to measure tumour size for solid lung cancers, part-solid adenocarcinomas, and for lung cancers removed after induction therapy. Despite the innovations, prognosis based on the anatomic extent is limited, because prognosis depends on factors related to the tumour, the patient and the environment. For the 9th edition, these factors, especially genetic biomarkers, will be combined in prognostic groups to refine prognosis at clinical and pathologic staging. To achieve this challenging objective, international cooperation is essential, and the IASLC Staging and Prognostic Factors Committee counts on it for the development of the 9th edition due to be published in 2024.

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