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Noman MK, Shamsul Islam SM, Jafar Jalali SM, Abu-Khalaf J, Lavery P. BAOS-CNN: A novel deep neuroevolution algorithm for multispecies seagrass detection. PLoS One 2024; 19:e0281568. [PMID: 38917071 PMCID: PMC11198790 DOI: 10.1371/journal.pone.0281568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 11/05/2023] [Indexed: 06/27/2024] Open
Abstract
Deep learning, a subset of machine learning that utilizes neural networks, has seen significant advancements in recent years. These advancements have led to breakthroughs in a wide range of fields, from natural language processing to computer vision, and have the potential to revolutionize many industries or organizations. They have also demonstrated exceptional performance in the identification and mapping of seagrass images. However, these deep learning models, particularly the popular Convolutional Neural Networks (CNNs) require architectural engineering and hyperparameter tuning. This paper proposes a Deep Neuroevolutionary (DNE) model that can automate the architectural engineering and hyperparameter tuning of CNNs models by developing and using a novel metaheuristic algorithm, named 'Boosted Atomic Orbital Search (BAOS)'. The proposed BAOS is an improved version of the recently proposed Atomic Orbital Search (AOS) algorithm which is based on the principle of atomic model and quantum mechanics. The proposed algorithm leverages the power of the Lévy flight technique to boost the performance of the AOS algorithm. The proposed DNE algorithm (BAOS-CNN) is trained, evaluated and compared with six popular optimisation algorithms on a patch-based multi-species seagrass dataset. This proposed BAOS-CNN model achieves the highest overall accuracy (97.48%) among the seven evolutionary-based CNN models. The proposed model also achieves the state-of-the-art overall accuracy of 92.30% and 93.5% on the publicly available four classes and five classes version of the 'DeepSeagrass' dataset, respectively. This multi-species seagrass dataset is available at: https://ro.ecu.edu.au/datasets/141/.
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Affiliation(s)
- Md Kislu Noman
- School of Science, Edith Cowan University, Perth, Australia
| | | | | | | | - Paul Lavery
- School of Science, Edith Cowan University, Perth, Australia
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2
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Hosseinzadeh M, Hussain D, Zeki Mahmood FM, A. Alenizi F, Varzeghani AN, Asghari P, Darwesh A, Malik MH, Lee SW. A model for skin cancer using combination of ensemble learning and deep learning. PLoS One 2024; 19:e0301275. [PMID: 38820401 PMCID: PMC11142560 DOI: 10.1371/journal.pone.0301275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/13/2024] [Indexed: 06/02/2024] Open
Abstract
Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively.
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Affiliation(s)
- Mehdi Hosseinzadeh
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
| | - Dildar Hussain
- Department of AI and Data Science, Sejong University, Seoul, Republic of Korea
| | | | - Farhan A. Alenizi
- Electrical Engineering Department, College of engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Parvaneh Asghari
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Aso Darwesh
- Department of Information Technology, University of Human Development, Sulaymaniyah, Kurdistan region of Iraq
| | - Mazhar Hussain Malik
- School of Computer Science and Creative Technologies College of Arts, Technology and Environment (CATE) University of the West of England Frenchay Campus, Coldharbour Lane Bristol, Bristol, United Kingdom
| | - Sang-Woong Lee
- Pattern Recognition and Machine Learning Lab, Gachon University, Seongnamdaero, Sujeonggu, Seongnam, Republic of Korea
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Rabinovici-Cohen S, Fridman N, Weinbaum M, Melul E, Hexter E, Rosen-Zvi M, Aizenberg Y, Porat Ben Amy D. From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma. Cancers (Basel) 2024; 16:1019. [PMID: 38473377 DOI: 10.3390/cancers16051019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies. Despite numerous advancements in understanding its biology, the mean five-year survival rate of OSCC is still very poor at about 50%, with even lower rates when the disease is detected at later stages. We investigate the use of clinical photographic images taken by common smartphones for the automated detection of OSCC cases and for the identification of suspicious cases mimicking cancer that require an urgent biopsy. We perform a retrospective study on a cohort of 1470 patients drawn from both hospital records and online academic sources. We examine various deep learning methods for the early detection of OSCC cases as well as for the detection of suspicious cases. Our results demonstrate the efficacy of these methods in both tasks, providing a comprehensive understanding of the patient's condition. When evaluated on holdout data, the model to predict OSCC achieved an AUC of 0.96 (CI: 0.91, 0.98), with a sensitivity of 0.91 and specificity of 0.81. When the data are stratified based on lesion location, we find that our models can provide enhanced accuracy (AUC 1.00) in differentiating specific groups of patients that have lesions in the lingual mucosa, floor of mouth, or posterior tongue. These results underscore the potential of leveraging clinical photos for the timely and accurate identification of OSCC.
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Affiliation(s)
| | - Naomi Fridman
- TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel
- The Department of Industrial Engineering & Management, Ariel University, Ariel 40700, Israel
| | - Michal Weinbaum
- TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel
| | - Eli Melul
- TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel
| | - Efrat Hexter
- IBM Research-Israel, Mount Carmel, Haifa 3498825, Israel
| | - Michal Rosen-Zvi
- IBM Research-Israel, Mount Carmel, Haifa 3498825, Israel
- Faculty of Medicine, The Hebrew University, Jerusalem 91120, Israel
| | - Yelena Aizenberg
- Oral Medicine Unit, Department of Oral and Maxillofacial Surgery, Tzafon Medical Center, Poriya 15208, Israel
| | - Dalit Porat Ben Amy
- Oral Medicine Unit, Department of Oral and Maxillofacial Surgery, Tzafon Medical Center, Poriya 15208, Israel
- The Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan 5290002, Israel
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Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med 2023; 165:107413. [PMID: 37703714 DOI: 10.1016/j.compbiomed.2023.107413] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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Affiliation(s)
- Nan Luo
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Xiaojing Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Luxin Su
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Zilin Cheng
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Wenyi Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Pingsheng Hao
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
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Kränke T, Tripolt-Droschl K, Röd L, Hofmann-Wellenhof R, Koppitz M, Tripolt M. New AI-algorithms on smartphones to detect skin cancer in a clinical setting-A validation study. PLoS One 2023; 18:e0280670. [PMID: 36791068 PMCID: PMC9931135 DOI: 10.1371/journal.pone.0280670] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 01/05/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The incidence of skin cancer is rising worldwide and there is medical need to optimize its early detection. This study was conducted to determine the diagnostic and risk-assessment accuracy of two new diagnosis-based neural networks (analyze and detect), which comply with the CE-criteria, in evaluating the malignant potential of various skin lesions on a smartphone. Of note, the intention of our study was to evaluate the performance of these medical products in a clinical setting for the first time. METHODS This was a prospective, single-center clinical study at one tertiary referral center in Graz, Austria. Patients, who were either scheduled for preventive skin examination or removal of at least one skin lesion were eligible for participation. Patients were assessed by at least two dermatologists and by the integrated algorithms on different mobile phones. The lesions to be recorded were randomly selected by the dermatologists. The diagnosis of the algorithm was stated as correct if it matched the diagnosis of the two dermatologists or the histology (if available). The histology was the reference standard, however, if both clinicians considered a lesion as being benign no histology was performed and the dermatologists were stated as reference standard. RESULTS A total of 238 patients with 1171 lesions (86 female; 36.13%) with an average age of 66.19 (SD = 17.05) was included. Sensitivity and specificity of the detect algorithm were 96.4% (CI 93.94-98.85) and 94.85% (CI 92.46-97.23); for the analyze algorithm a sensitivity of 95.35% (CI 93.45-97.25) and a specificity of 90.32% (CI 88.1-92.54) were achieved. DISCUSSION The studied neural networks succeeded analyzing the risk of skin lesions with a high diagnostic accuracy showing that they are sufficient tools in calculating the probability of a skin lesion being malignant. In conjunction with the wide spread use of smartphones this new AI approach opens the opportunity for a higher early detection rate of skin cancer with consecutive lower epidemiological burden of metastatic cancer and reducing health care costs. This neural network moreover facilitates the empowerment of patients, especially in regions with a low density of medical doctors. REGISTRATION Approved and registered at the ethics committee of the Medical University of Graz, Austria (Approval number: 30-199 ex 17/18).
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Affiliation(s)
- Teresa Kränke
- Department of Dermatology and Venereology, Medical University of Graz, Graz, Austria
- * E-mail:
| | | | - Lukas Röd
- Medical University of Graz, Graz, Austria
| | | | | | - Michael Tripolt
- Department of Dermatology and Venereology, Medical University of Graz, Graz, Austria
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Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis. Diagnostics (Basel) 2023; 13:diagnostics13040699. [PMID: 36832186 PMCID: PMC9955143 DOI: 10.3390/diagnostics13040699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/01/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023] Open
Abstract
Histopathology is the most accurate way to diagnose cancer and identify prognostic and therapeutic targets. The likelihood of survival is significantly increased by early cancer detection. With deep networks' enormous success, significant attempts have been made to analyze cancer disorders, particularly colon and lung cancers. In order to do this, this paper examines how well deep networks can diagnose various cancers using histopathology image processing. This work intends to increase the performance of deep learning architecture in processing histopathology images by constructing a novel fine-tuning deep network for colon and lung cancers. Such adjustments are performed using regularization, batch normalization, and hyperparameters optimization. The suggested fine-tuned model was evaluated using the LC2500 dataset. Our proposed model's average precision, recall, F1-score, specificity, and accuracy were 99.84%, 99.85%, 99.84%, 99.96%, and 99.94%, respectively. The experimental findings reveal that the suggested fine-tuned learning model based on the pre-trained ResNet101 network achieves higher results against recent state-of-the-art approaches and other current powerful CNN models.
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A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity. PLoS One 2022; 17:e0269826. [PMID: 35925956 PMCID: PMC9352099 DOI: 10.1371/journal.pone.0269826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 05/30/2022] [Indexed: 11/19/2022] Open
Abstract
The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting overfitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the ‘box blur’ down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted.This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a lightweighted network using a limited amount of training data.
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8
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Hasan MK, Elahi MTE, Alam MA, Jawad MT, Martí R. DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2021.100819] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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9
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Takiddin A, Schneider J, Yang Y, Abd-Alrazaq A, Househ M. Artificial Intelligence for Skin Cancer Detection: Scoping Review. J Med Internet Res 2021; 23:e22934. [PMID: 34821566 PMCID: PMC8663507 DOI: 10.2196/22934] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 01/05/2021] [Accepted: 08/03/2021] [Indexed: 01/12/2023] Open
Abstract
Background Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. Objective The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. Methods We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.
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Affiliation(s)
- Abdulrahman Takiddin
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States.,College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Jens Schneider
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Yin Yang
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Alaa Abd-Alrazaq
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Daneshjou R, Smith MP, Sun MD, Rotemberg V, Zou J. Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review. JAMA Dermatol 2021; 157:1362-1369. [PMID: 34550305 DOI: 10.1001/jamadermatol.2021.3129] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical care, but fair, generalizable algorithms depend on the clinical data on which they are trained and tested. Objective To assess whether data sets used for training diagnostic AI algorithms addressing skin disease are adequately described and to identify potential sources of bias in these data sets. Data Sources In this scoping review, PubMed was used to search for peer-reviewed research articles published between January 1, 2015, and November 1, 2020, with the following paired search terms: deep learning and dermatology, artificial intelligence and dermatology, deep learning and dermatologist, and artificial intelligence and dermatologist. Study Selection Studies that developed or tested an existing deep learning algorithm for triage, diagnosis, or monitoring using clinical or dermoscopic images of skin disease were selected, and the articles were independently reviewed by 2 investigators to verify that they met selection criteria. Consensus Process Data set audit criteria were determined by consensus of all authors after reviewing existing literature to highlight data set transparency and sources of bias. Results A total of 70 unique studies were included. Among these studies, 1 065 291 images were used to develop or test AI algorithms, of which only 257 372 (24.2%) were publicly available. Only 14 studies (20.0%) included descriptions of patient ethnicity or race in at least 1 data set used. Only 7 studies (10.0%) included any information about skin tone in at least 1 data set used. Thirty-six of the 56 studies developing new AI algorithms for cutaneous malignant neoplasms (64.3%) met the gold standard criteria for disease labeling. Public data sets were cited more often than private data sets, suggesting that public data sets contribute more to new development and benchmarks. Conclusions and Relevance This scoping review identified 3 issues in data sets that are used to develop and test clinical AI algorithms for skin disease that should be addressed before clinical translation: (1) sparsity of data set characterization and lack of transparency, (2) nonstandard and unverified disease labels, and (3) inability to fully assess patient diversity used for algorithm development and testing.
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Affiliation(s)
- Roxana Daneshjou
- Stanford Department of Dermatology, Stanford School of Medicine, Redwood City, California.,Stanford Department of Biomedical Data Science, Stanford School of Medicine, Stanford, California
| | - Mary P Smith
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mary D Sun
- currently a medical student at Icahn School of Medicine at Mount Sinai, New York, New York
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James Zou
- Department of Electrical Engineering, Stanford University, Stanford, California.,Department of Biomedical Data Science, Stanford University, Stanford, California.,Chan Zuckerberg Biohub, San Francisco, California
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Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. Eur J Cancer 2021; 156:202-216. [PMID: 34509059 DOI: 10.1016/j.ejca.2021.06.049] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/18/2021] [Accepted: 06/28/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice. OBJECTIVE The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians. METHODS PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included. RESULTS A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images. CONCLUSIONS All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.
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12
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A benchmark for neural network robustness in skin cancer classification. Eur J Cancer 2021; 155:191-199. [PMID: 34388516 DOI: 10.1016/j.ejca.2021.06.047] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/18/2021] [Accepted: 06/29/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. OBJECTIVE The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. METHODS Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. RESULTS The benchmark contains three data sets-Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. CONCLUSIONS This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.
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Hasan MK, Roy S, Mondal C, Alam MA, E Elahi MT, Dutta A, Uddin Raju ST, Jawad MT, Ahmad M. Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoders. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102661] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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14
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Feng XY, Xu X, Zhang Y, Xu YM, She Q, Deng B. Application of convolutional neural network in detecting and classifying gastric cancer. Artif Intell Gastrointest Endosc 2021. [DOI: 10.37126/aige.v2.i3.70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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15
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Feng XY, Xu X, Zhang Y, Xu YM, She Q, Deng B. Application of convolutional neural network in detecting and classifying gastric cancer. Artif Intell Gastrointest Endosc 2021; 2:71-78. [DOI: 10.37126/aige.v2.i3.71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/21/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer (GC) is the fifth most common cancer in the world, and at present, esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC. Convolutional neural networks (CNNs) are a type of deep learning model and have been widely used for image analysis. This paper reviews the application and prospects of CNNs in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.
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Affiliation(s)
- Xin-Yi Feng
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
| | - Xi Xu
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
| | - Yun Zhang
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
| | - Ye-Min Xu
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
| | - Qiang She
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
| | - Bin Deng
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
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16
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Tamoor M, Younas I. Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:721-739. [PMID: 34024808 DOI: 10.3233/xst-210879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.
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Affiliation(s)
- Maria Tamoor
- FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan
| | - Irfan Younas
- FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan
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17
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Abd ElGhany S, Ramadan Ibraheem M, Alruwaili M, Elmogy M. Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network. COMPUTERS, MATERIALS & CONTINUA 2021; 68:117-135. [DOI: 10.32604/cmc.2021.016102] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/24/2021] [Indexed: 09/02/2023]
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18
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Aziz F, Ahmad T, Malik AH, Uddin MI, Ahmad S, Sharaf M. Reversible data hiding techniques with high message embedding capacity in images. PLoS One 2020; 15:e0231602. [PMID: 32469877 PMCID: PMC7259517 DOI: 10.1371/journal.pone.0231602] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 03/26/2020] [Indexed: 11/24/2022] Open
Abstract
Reversible Data Hiding (RDH) techniques have gained popularity over the last two decades, where data is embedded in an image in such a way that the original image can be restored. Earlier works on RDH was based on the Image Histogram Modification that uses the peak point to embed data in the image. More recent works focus on the Difference Image Histogram Modification that exploits the fact that the neighbouring pixels of an image are highly correlated and therefore the difference of image makes more space to embed large amount of data. In this paper we propose a framework to increase the embedding capacity of reversible data hiding techniques that use a difference of image to embed data. The main idea is that, instead of taking the difference of the neighboring pixels, we rearrange the columns (or rows) of the image in a way that enhances the smooth regions of an image. Any difference based technique to embed data can then be used in the transformed image. The proposed method is applied on different types of images including textures, patterns and publicly available images. Experimental results demonstrate that the proposed method not only increases the message embedding capacity of a given image by more than 50% but also the visual quality of the marked image containing the message is more than the visual quality obtained by existing state-of-the-art reversible data hiding technique. The proposed technique is also verified by Pixel Difference Histogram (PDH) Stegoanalysis and results demonstrate that marked images generated by proposed method is undetectable by PDH analysis.
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Affiliation(s)
- Furqan Aziz
- Center of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan
- Centre for Computational Biology, University of Birmingham, Birmingham, England, United Kingdom
| | - Taeeb Ahmad
- Center of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan
| | - Abdul Haseeb Malik
- Department of Computer Science, University of Peshawar, Peshawar, Pakistan
| | - M. Irfan Uddin
- Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan
- * E-mail:
| | - Shafiq Ahmad
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Mohamed Sharaf
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Kingdom of Saudi Arabia
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19
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Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.034] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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20
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Li L, Chen Y, Shen Z, Zhang X, Sang J, Ding Y, Yang X, Li J, Chen M, Jin C, Chen C, Yu C. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer 2020; 23:126-132. [PMID: 31332619 PMCID: PMC6942561 DOI: 10.1007/s10120-019-00992-2] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 07/12/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Magnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI. METHODS A total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS The sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts. CONCLUSIONS Our CNN system showed high accuracy, sensitivity and specificity in the diagnosis of early gastric cancer. It is anticipated that more progress will be made in optimization of the CNN diagnostic system and further development of artificial intelligence in the medical field.
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Affiliation(s)
- Lan Li
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Yishu Chen
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Zhe Shen
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Xuequn Zhang
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Jianzhong Sang
- Department of Gastroenterology, Yuyao People’s Hospital, Yuyao, China
| | - Yong Ding
- grid.203507.30000 0000 8950 5267Department of Gastroenterology, The Affiliated Hospital of School of Medicine of Ningbo University, Ningbo, China
| | - Xiaoyun Yang
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Jun Li
- grid.13402.340000 0004 1759 700XDepartment of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ming Chen
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Chaohui Jin
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Chunlei Chen
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Chaohui Yu
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
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