Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Sampathila, Niranjana; Chadaga, Krishnaraj; Goswami, Neelankit; Chadaga, Rajagopala; Pandya, Mayur; Prabhu, Srikanth; Bairy, Muralidhar G.; Swathi, K. S.; Bhat, Devadas; P, Sudhakara Upadya

doi:10.3390/healthcare10101812

Cited by 35 publications

(22 citation statements)

References 43 publications

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“…Our method offers a full view of a particular site, which enables us to identify the disease, as well as interior areas that have been infected with it. Dermoscopy is the most reliable [ 41 ] and time-effective [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ] approach for determining if a lesion is a BCC, MEL, SCC, or MN. A computerized diagnostic approach is required to identify BCC, MEL, SCC, and MN, since the number of confirmed cases of deadly skin cancer is continually growing [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

“…These images were represented by bkl, mel, and nv. Krishnaraj et al [ 52 ] designed machine learning [ 53 , 54 , 55 , 56 ] classifiers that identified binary classes of cervical cancer, such as adenosquamous carcinoma and SCC. They collected the dataset at the University of California, Irvine (UCI) repository, and the Borderline-SMOTE approach was employed to balance the unbalanced data.…”

Section: Literature Reviewmentioning

confidence: 99%

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DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

Tahir

Naeem

Malik

et al. 2023

Cancers

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Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

Tahir

Naeem

Malik

et al. 2023

Cancers

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“…AI algorithms are being developed and trained to identify patterns and features in medical images, such as X-rays, CT scans, and MRI scans, indicative of various diseases, enabling earlier and more accurate diagnoses. There are many studies related to artificial intelligence in medicine including the articles “AI-Assisted Tuberculosis Detection”, “Classification of Malaria Using Object Detection Models”, “Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images”, “Uses of AI in Ultrasound Imaging”, and “Machine Learning in Prostate MRI for Prostate Cancer” [ 31 , 32 , 33 , 34 , 35 ]. The integration of AI into medical imaging is an exciting development that has brought about many changes.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review on Diagnostic Reference Levels for Computed Tomography Examinations in Radiation Therapy Planning

Rao

Sharan

Sukumar³

et al. 2023

Diagnostics

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Eligibility criteria: A cohort study (observational design) reporting DRLs in adult patients undergoing computed tomography (CT) for radiation therapy for the region head and neck or pelvis were included. The comprehensive literature searches for the relevant studies published between 2000 and 2021 were performed using PubMed, Scopus, CINHAL, Web of Science, and ProQuest. Results: Three hundred fifty-six articles were identified through an extensive literature search. Sixty-eight duplicate reports were removed. The title and abstract of 288 studies were assessed and excluded if they did not meet the inclusion criteria. Sixteen of 288 articles were selected for full-text screening (studies conducted between 2000 and 2021). Five articles were included in the review after the full-text screening. Conclusion: A globally approved standard protocol that includes scanning techniques, dose measurement method, and DRL percentile needs to be established to make a valuable and accurate comparison with international DRLs.

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“…The model achieved an accuracy of 91.1% for diagnosing the C-NMC dataset. Niranjana et al [ 13 ] converted images to HIS color space, segmented WBC cells, then trained the ALLNET model and tested its performance, which achieved an accuracy of 95.54% and a sensitivity of 95.91%. Pradeep et al [ 14 ] used three CNN models to extract image features of an ALL dataset and classify them by RF and SVM.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

et al. 2023

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Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

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Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Cited by 35 publications

References 43 publications

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

Systematic Review on Diagnostic Reference Levels for Computed Tomography Examinations in Radiation Therapy Planning

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

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