Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review
Abstract
:1. Introduction
2. Research Methodology
2.1. Research Questions
2.2. Research Strategy
2.3. Criteria for Article Selection
- Articles using the most up-to-date techniques for analyzing medical images.
- Articles that were written in the English language.
- Articles published in the last five years (2017–2021).
- Studies that were presented at a peer-reviewed conferences or journals.
- Following the definition of the inclusion criteria, the following exclusion criteria were determined:
- Duplicate references from the various electronic archives that were searched.
- Articles with a page count of less than four.
- Articles that fail to respond to any of the research questions.
- Articles that were written in a language other than English
- Articles that did not address the study’s goals.
2.4. Research Results
3. Results of Systematic Review
3.1. Medical Imaging Modalities
3.2. Medical Image Analysis
3.2.1. Medical Image Preprocessing
3.2.2. Segmentation Techniques
3.2.3. Feature Extraction Techniques
3.2.4. Classification Techniques
3.2.5. Metric Evaluation
4. Machine Learning Techniques
5. Deep Learning
6. Diseases Diagnosis System
7. Discussion and Future Directions
7.1. Answer to Research Questions
7.2. Future Directions
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
References
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Studies (Author (Year) [Ref]) | Imaging Modality | Type of Disease | Medical Database |
---|---|---|---|
Danni Cheng et al. (2017) [66] | PET | Alzheimer’s disease | Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/) (accessed on 18 June 2022). The dataset contained 339 brain images (93 AD, 146 MCI, 100 NC subjects). |
Syrine Neffati et al. (2017) [67] | MRI | Brain disease | Harvard Medical School website, Open Access Series of Imaging Studies (OASIS) website. The dataset contained normal brains and seven types of pathological brains with a total of 226 images (38 normal brains and 188 pathological brains). |
Varun Jain et al. (2017) [68] | MRI | Brain tumor | SICAS Medical Image Repository dataset contained 25 MRI brain images (20 benign, 5 malignant). |
Anjukrishna et al. (2017) [69] | CT | Liver cancer tumor | Travancore scan center, Thiruvananthapuram (www.liveratlas.org) (accessed on 19 June 2022). The dataset contained 80 abdominal CT images, 20 of a normal liver and the rest images of various liver diseases. |
Shouvik Chakraborty et al. (2017) [70] | Dermoscopy skin imaging | Skin cancers | International Skin Imaging Collaboration (ISIC) dataset contained images of two classes (skin angioma and basel cell carcinoma). |
Soumya Sourav et al. (2017) [71] | Dermoscopy skin imaging | Dermatological diseases | Dermatology Online Atlas (www.dermis.net), http://homepages.inf.ed.ac.uk/rbf/DERMOFIT/ (accessed on 19 June 2022). The dataset contained 3000 images of four types (psoriasis, herpes, eczema, and melanoma). |
Priyanka Lodha et al. (2018) [72] | MRI PET | Alzheimer’s disease | ADNI database (http://adni.loni.usc.edu) (accessed on 18 June 2022). The ADNI study was applied to people between the age of 55 and 90. |
Keerthana T K et al. (2018) [73] | MRI | Brain tumor | Brain MRI medical image dataset which contained normal, benign, and malignant images. |
Latika A. Thamke et al. (2018) [74] | CT | Lung diseases | CT scan image dataset collected from patients (age ranges from 35 to 75). The datasets contained 400 images (100 normal, 100 pleural effusion, 100 bronchitis, and 100 emphysema). |
Abid Sarwar et al. (2018) [75] | Medical data (non-image) | Diabetes type-II | The authors prepared a rich database that included two classes (diabetic and non-diabetic) of 400 people from a large geographical area (age ranges from 5 to 75). |
Pallavi. B et al. (2019) [76] | Dermoscopy imaging | Malignant melanoma skin cancer disease | The authors gathered specimen images of the sickly greeneries, then trained and stored them in the database. This database contained normal and abnormal images. |
Neeraj Kumar et al. (2019) [77] | CT | Bone disease (osteoporosis) | The NCBI dataset associated with osteoporosis. The authenticated medical center Medpix NLM website. The database contained two classes with features (plane, modality, age, fracture, gender, weight, and history). |
Rabi et al. (2019) [78] | Endoscopic images | Gastrointestinal (GI) diseases | The KVASIR dataset consisted of 4000 images containing 8 classes of GI diseases. Some of the supplied image classes feature a green image depicting the location and form of the endoscope within the intestine. |
Sakshi Sharma et al. (2019) [79] | CT | Lung cancer disease | Database gathered from the IMBA web page contained normal and abnormal CT images of cancers for both males and females. |
Smir S. Yadav et al. (2019) [80] | X-ray | Pneumonia disease | The dataset was based on previous literature. The dataset contained 5856 images (normal, bacteria, and viruses). |
Sannasi Chakravarthy et al. (2019) [81] | CT | Lung cancer disease | Lung Image Database Consortium (LIDC). The dataset was composed of diagnostic and cancer screening thoracic CAT examinations with marked-up interpretations. |
Aamir Bhat et al. (2019) [82] | X-ray | Osteoarthritis disease | The datasets were gathered from numerous hospitals. The dataset contained 126 knee joint X-ray images. |
Mohammed Aledhari et al. (2019) [83] | X-ray chest radiographs | Pneumonia disease | National Institute of Health (NIH) dataset contained 1431 labeled X-ray images (normal and pneumonia). |
Maciej Szymkowski et a l. (2020) [84] | Retina color images | Retina disease diagnosis | Medical University of Bialystok (MUB) Clinic Hospital, publicly available DRIVE STARE, Kaggle. The database contained 500 images (250 healthy samples and 250 pathological samples). |
Shashank Awasthi et al. (2020) [85] | MRI | Alzheimer’s disease | The publicly available OASIS dataset contained MRI images of normal and AD patients. |
Halebeedu Suresha et al. (2020) [86] | MRI | Alzheimer’s disease | The National Institute of Mental Health and Neurosciences (NIMHANS) dataset contained 800 images for 99 people (60 normal and 39 AD with age ranges from 55 to 87 years). The ADNI dataset contained 819 subjects (229 normal, 192 AD, and 398 MCI). |
Chiranji Lal Chowdhary et al. (2020) [87] | Breast X-ray (mammogram) | Breast cancer disease | Mammography Image Analysis Society (MIAS) dataset contained 320 mammogram images (51 malignant, 63 benign, and 206 normal). |
Fateme Gholami et al. (2020) [88] | MRI | Brain tumors | MRI image dataset gathered by the authors. The number of samples considered for evaluation in this article was 30 gray images. |
Jaspreet Kaur et al. (2020) [89] | CT PET | Detecting cancer | PET Center, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. The dataset contained 200 medical images. |
Begüm Erkal et al. (2020) [90] | Medical data (non-image) | Brain cancer | Broad Institute (http://portals.broadinstitute.org/cgi-bin/cancer/datasets) (accessed on 20 June 2022). The dataset contained 42 samples (7129 features and 5 classes). |
G U Santosh Kumar et al. (2021) [91] | MRI | Cardiovascular diseases, cardiac attack | The dataset of patients from the York university contained the cardiac MRI DICOM images of patients suffering from various cardiovascular diseases. |
Nasr Gharaibeh et al. (2021) [92] | Retinal fundus images | Diabetic retinopathy (DR) | The Image-Ret database included two sub-databases (i.e., DIARETDB0 and DIARETDB1). |
Laiba Zubair et al. (2021) [93] | MRI | Alzheimer’s disease | The ADNI dataset contained 145 MRI images (39 AD, 45 CN, and 68 MCI). |
Mehedi Masud et al. (2021) [94] | Histopathological image | Lung cancer and colon cancer | The LC25000 dataset contained 25,000 color images of five types of lung and colon tissues (colon adenocarcinoma, benign colonic tissue, lung adenocarcinoma, benign lung tissue, and lung squamous cell carcinoma). |
Muhammad Assam et al. (2021) [95] | MRI | MRI brain images classification | Harvard medical school database contained 70 images (25 normal and 45 abnormal, which comprised three different kinds of diseases: brain tumor, acute stroke, and Alzheimer disease). |
Antor Hashan et al. (2021) [96] | MRI | Brain tumor | MRI images of the brain were collected from various hospitals and compiled in a Kaggle dataset that contained 400 images (230 brain tumors and 170 normal) |
Xiuzhen Cai et al. (2021) [97] | Breast X-ray (mammogram) | Breast cancer | The MIAS mammogram database (http://peipa.esex.ac.uk/info/mias.html) (accessed on 21 June 2022). The dataset contained 322 mammography images that were taken from the UK National Breast Screening Program. |
Makineni Kumar et al. (2021) [98] | MRI | Lung cancer disease | MRI lung cancer image dataset, which contained normal and tumor images. |
Md Riajuliislam et al. (2021) [99] | Medical data (non-image) | Thyroid disease (hypothyroid) | The dataset from the registered diagnostic center Dhaka, Bangladesh, contained 519 data with 9 attributes. |
Aasawari M. Patankar et al.(2021) [100] | Fundus images | Ophthalmic diseases | The dataset was gathered by the authors and contained various ophthalmic diseases such as macular degeneration, retinopathy, myopia, cataract, and other abnormalities. |
Deepak R. Parashar et al. (2021) [101] | Retinal fundus images | Glaucoma classification | RIM-ONE dataset contained 455 fundus photographs. Drishti-GS1 dataset contained 101 retinal images. RIM-ONE released 1 dataset which contained 40 images. |
Majdah Alshammari et al. (2021) [102] | MRI | Alzheimer’s disease | Alzheimer’s dataset (available: https://www.kaggle.com/tourist55/alzheimers) (accessed on 22 June 2022) contained 4 classes of diseases (896 mild demented, 64 moderate demented, 2240 very mild demented, and 3200 non-demented). |
Mohammed Al-Smadi et al. (2021) [103] | Fundus images | Diabetic retinopathy | Kaggle dataset (available: https://www.kaggle.com/c/aptos2019-blindness-detection) (accessed on 22 June 2022) contained 3562 images and was obtained from various clinics in India and represents real-world data. |
Saeed Mohagheghi et al. (2021) [104] | X-ray CT | COVID-19 disease | Kaggle dataset contained 1400 healthy and pneumonia images. J. Cohen’s COVID-19 dataset contained 210 COVID-19, normal, and pneumonia images. |
Sonit Singh et al. (2021) [105] | CT, MRI, X-ray, PET, US, Microscopy images | Classifying medical image modalities | The authors downloaded 10,000 medical images for each modality from Open-i Biomedical Image Search Engine, National Institute of Health, and U.S. National Laboratory of Medicine. |
Studies (Author (Year) [Ref]) | Type of Features | Method Used |
---|---|---|
Syrine Neffati et al. (2017) [67] | Texture features | DWT transforms to extract features. The kernel PCA (KPCA) technique for feature reduction. |
Varun Jain et al. (2017) [68] | Texture features | DWT transform for feature extraction. PCA technique for diminishing the number of features. |
AnjKrishna M et al. (2017) [69] | Texture features | SFTA and modified SFTA algorithms to extract features. |
Shouvik Chakraborty et al. (2017) [70] | Several key points such as features from which it creates a descriptor for that point | SIFT to detect key (interest) points and feature extraction. Bag-of-features concept to decrease the number of key points. |
Priyanka Lodha, et al. (2018) [72] | Cognitive and biological features | The full volume of the brain is extracted from MRI images and other cognitive and biological features. |
Keerthana T K et al. (2018) [73] | Texture features | GLCM for features extraction. |
Latika A. Thamke et al. (2018) [74] | Texture and shape features Pixel coefficient value | GLCM for features extraction. Moment Invariant (MI). WHT transforms to calculate the pixel value of the image. |
Pallavi. B et al. (2019) [76] | Combination of texture and color features | Nine features are mean, standard deviation, entropy, RMS, variance, smoothness, kurtosis, skewness, and inverse difference momentum. |
Bahaa Rabi et al. (2019) [78] | Texture and shape features | DWT transforms for feature extraction. HOG transforms for feature extraction. PCA and SVD methods for feature reduction. |
Sannasi Chakravarthy et al. (2019) [81] | Texture features | GLCM for feature extraction. CCSA for feature selection manually. |
Aamir Bhat et al. (2019) [82] | Texture features | HOG and DWT transform for feature extraction. |
Maciej Szymkowsk et al. (2020) [84] | Biometric features | Extract the characteristic points (so-called minutiae) on retina images and count the number of minutiae on the resulting image. |
Shashank Awasthi et al. (2020) [85] | Combination of fractal and statistical features | The features such as mean of zero crossing, mean of IMF, standard deviation, etc. PCA for feature reduction. |
Halebeedu Suresha et al. (2020) [86] | Texture features | HOG transforms for feature extraction. |
Chiranji Lal Chowdhary et al. (2020) [87] | Vital features for segmentation Texture features for classification | The vital features are texture, shape, margin, and intensity. The gray-level histogram computations compute the texture features. |
Jaspreet Kaur et al. (2020) [89] | Texture features | GLCM technique for feature extraction. |
Nasr Gharaibeh et al. (2021) [92] | Texture and shape features | Haralick and shape-based features. US-PSO-RR algorithm for feature reduction. |
Mehedi Masud et al. (2021) [94] | Texture features | 2D Fourier Features (2D-FFT) and 2D Wavelet Features (2D-DWT). |
Muhammad Assam et al. (2021) [95] | Color features | DWT transforms for feature extraction. Color Moments (CMs) to reduce the number of features. |
Xiuzhen Cai et al. (2021) [97] | Texture features | Combination of GLCM and DWT. |
Makineni Kumar et al. (2021) [98] | Shape features | Diameter, Perimeter, Entropy, Intensity, and Eccentricity. |
Md Riajuliislam et al. (2021) [99] | Data of patients | Age, sex, ID, etc. PCA for feature selection. |
Aasawari M. Patankar et al. (2021) [100] | Texture, color, and edges features | Wavelet transform, DCT approach, and color information. |
Deepak R. Parashar et al. (2021) [101] | Texture features | Texture-based Zernike moment, chip histogram, and Haralick descriptors. |
Sonit Singh el at. (2021) [105] | Statistical and texture features | Local binary pattern (LBP) and GLCM. |
Studies [Ref] | Performance Metrics | No. of Studies |
---|---|---|
[68,69,71,73,75,76,77,84,85,89,95,100,102,103,105] | Accuracy | 15 |
[67,79,81,88,92,93,97,101] | Accuracy, Specificity, Sensitivity | 8 |
[70,82,86,94] | Accuracy, Precision, Recall, F1 score | 4 |
[72,74,98,104] | Accuracy, Specificity, Sensitivity, Precision, Recall, F1 score | 4 |
[90,96] | Accuracy, F1 score | 2 |
[66] | Accuracy, Specificity, Sensitivity, AUC | 1 |
[80] | Accuracy, Specificity, Recall | 1 |
[78] | Accuracy, Specificity, Precision, Recall, F1 score | 1 |
[87] | Accuracy, Sensitivity | 1 |
[83] | Accuracy, Specificity | 1 |
[99] | Accuracy, Specificity, Sensitivity, F1 score | 1 |
Studies (Author (Year) [Ref]) | Techniques | Task | Accuracy Results |
---|---|---|---|
Anju krishna M et al. (2017) [69] | Naïve Bayes and SVM | Classify liver tumor: Normal, Cirrhosis, HCC, and Hemangioma | 78% 92.5% |
Priyanka Lodha, et al. (2018) [72] | SVM, Gradient boosting, NN, K-NN, and RF | Alzheimer’s disease | 97.56% 97.25% 98.36% 95% 97.86% |
Keerthana T K et al. (2018) [73] | SVM | Diagnosis and classification of brain tumor disease | The system provided better accuracy with the genetic algorithm GA-SVM. |
Latika A. Thamke et al. (2018) [74] | K-NN, Multiclass-SVM, and DT | Classify lung disease kinds: Normal, Bronchitis, Pleural, Emphysema, and Effusion | The K-NN classifier gave better outcomes (97.5%) than other classifiers. |
Abid Sarwar et al. (2018) [75] | ANN, SVM, K-NN, Naïve Bayes, and Ensemble | Diabetes type-II | The results showed that the ensemble technique provided a superior accuracy of 98.60%. |
Pallavi. B et al. (2019) [76] | Multi-level SVM | Classify skin disease | A combination of texture and color features outcomes in the highest classification accuracy using multi SVM. |
Neeraj Kumar et al. (2019) [77] | SVM and NN | Bone disease prediction of osteoporosis | Both classifiers gave efficient outcomes. |
Bahaa Rabi et al. (2019) [78] | SVM, K-NN, LD, and DT | Classify eight GI classes | The highest accuracy of classification was 99.8%, using the decision tree. |
Sannasi Chakravarthy et al. (2019) [81] | PNN | Lung cancer at the early stage | 90% |
Aamir Bhat et al. (2019) [82] | SVM and ANN | Knee osteoarthritis in early stage | 85.33% 73.82% |
Maciej Szymkowski et al. (2020) [84] | SVM (linear), SVM (3rd-degree polynomial), K-NN, and K-Means | The healthy and unhealthy | 95.25% 96.45% 73.96% 80.42% |
Shashank Awasthi et al. (2020) [85] | Logistic regression, Naïve Bayes, and SVM | Alzheimer’s disease classification in MRI image | 81% 79.88% 92.34% |
Chiranji Lal Chowdhary et al. (2020) [87] | DT, RSDA, SVM, and FSVM | Detecting breast cancer | 82.5% 96.1% 88.13% 98.85% |
Jaspreet Kaur et al. (2020) [89] | SVM and K-NN | Detecting cancer (cancerous and non-cancerous) | The accuracy of SVM varied from 95.5–98%. Accuracy of t h e K- NN classifier varied from 69.5–95.5%. |
Begüm Erkal et al. (2020) [90] | Random Forest, K-NN, Bayes, LMT, DT, and Multilayer Perceptron. | Detecting brain cancer | The experimental outcomes suggest that the Multilayer Perceptron approach outperforms other machine learning methods in accuracy. |
Md Riajuliislam et al. (2021) [99] | SVM, DT, RF, Naïve Bayes, and Logistic egression | Hypothyroid at the early stage | 99.35% 99.35% 99.35% 94.23% 99.35% |
Deepak R. Parashar et al. (2021) [101] | Multi-stage classifier (SVM) | Glaucoma classification | 91% |
Studies (Author (Year) [Ref]) | Architecture | Task | Results |
---|---|---|---|
Danni Cheng et al. (2017) [66] | CNN + RNN | Classify AD vs. NC Classify MCI vs. NC | 91.19% (Accuracy), 91.40% (Sensitivity), 91.00% (Specificity), and 95.28% (AUC). 78.86% (Accuracy), 78.08% (Sensitivity), 80.00% (Specificity), and 83.90% (AUC). |
Smir S. Yadav et al. (2019) [80] | VGG16 InceptionV3 Capsule Net | Classify pneumonia vs. normal | 0.923 (Accuracy), 0.926 (Specificity), and 0.923 (Recall) 0.824 (Accuracy), 0.846 (Specificity), and 0.824 (Recall) |
Mohammed Aledhari et al. (2019) [83] | VGG16 ResNet-50 Inception v3 (fine-tuned VGG16) | Pneumonia vs. normal | 68% (Accuracy) 58% (Accuracy) 53% (Accuracy) 75% (Accuracy) |
Maciej Szymkowski et al. (2020) [84] | ResNet50 | Retina diagnosis | 86% (Accuracy) |
Halebeedu Subbaraya et al. (2020) [86] | CNN + Adam Optimizer | Classify brain tumors | 90% (Accuracy) and 0.89 (F1 score) |
Laiba Zubair et al. (2021) [93] | CNN+ Bayesian optimization | AD vs. CN vs. MCI | 99.3% (Accuracy) |
Mehedi Masud et al. (2021) [94] | CNN | Five types of lungs and colon cancers | 96.33% (Accuracy), 96.39% (Precision), 96.37% (Recall), 96.38% (F-Measure) |
Xiuzhen Cai et al. (2021) [97] | CNN + Thermal Exchange Optimizer | Breast cancer diagnosis | 93.79 (Accuracy), 96.89 (Sensitivity), and 67.7 (Specificity). |
Makineni Kumar et al. (2021) [98] | LTD-CNN | Lung tumor detection | 96% (Accuracy), 96% (Sensitivity), 93% (Specificity), and 94% (Precision). |
Majdah Alshammari et al. (2021) [102] | CNN + ML+ Adam Optimizer | AD vs. mild demented vs. moderate demented vs. very mild demented, vs. non-demented | 98% accuracy for testing and 97% in training. |
Mohammed Al-Smadi et al. (2021) [103] | ResNet, Inception V3 Inception V4 DenseNet Xception EfficientNet | Diabetic retinopathy diagnosis | 77.6% (QWK) 82% (QWK) 79.6% (QWK) 81.8% (QWK) 80.9% (QWK) 80% (QWK) |
Saeed Mohagheghi et al. (2021) [104] | CNN with CBMIR | COVID-19 disease diagnosis | 97% (Accuracy), 99% (Sensitivity), 99% (Specificity 97% (Precision), 99% (Recall), and 98% (F-Measure). |
Sonit Singh el at. (2021) [105] | VGG-16 VGG-19 ResNet-50 Inception-v3 Xception MobileNet Inception-ResNet v2 | Classifying medical image modalities | 62% (Accuracy) 98.18% (Accuracy) 90% (Accuracy) 99% (Accuracy) 98.36% (Accuracy) 98.73% (Accuracy) 98.18% (Accuracy) |
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Rashed, B.M.; Popescu, N. Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review. Sensors 2022, 22, 7065. https://doi.org/10.3390/s22187065
Rashed BM, Popescu N. Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review. Sensors. 2022; 22(18):7065. https://doi.org/10.3390/s22187065
Chicago/Turabian StyleRashed, Baidaa Mutasher, and Nirvana Popescu. 2022. "Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review" Sensors 22, no. 18: 7065. https://doi.org/10.3390/s22187065