A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

Göreke, Volkan

doi:10.1007/s12539-023-00560-4

Cited by 6 publications

(3 citation statements)

References 64 publications

(52 reference statements)

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“…In 2023, Goreke 21 have developed a computer‐aided diagnosis system that can accurately categorize thyroid nodules based on information obtained from ultrasound scans. A qualified medical professional acquired and labeled the sub‐images.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules

Gulame,

Dixit

2024

Numer Methods Biomed Eng

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Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it challenging for a clinician to differentiate between malignant and benign forms based only on visual identification. The advancement of AI, particularly DL, provides significant breakthroughs in the domain of medical image identification. Yet, there are certain obstacles to achieving accuracy as well as efficacy in thyroid nodule detection. The thyroid nodules in this study are detected and classified using an inventive hybrid deep learning‐assisted multi‐classification method. The median blur method is applied in this work to eliminate the salt and pepper noise from the image. Then MPIU‐Net‐based segmentation is utilized to segment the image. The LGBPNP‐based features are retrieved from the segmented image to obtain a single histogram sequence of the LGBP pattern in addition to other features like extraction of multi‐texton and LTP‐based features. After the feature extraction, the data augmentation process is applied and then the features are fed to the hybrid classification‐based nodule classification model that comprises Deep Maxout and CNN, this hybrid classification trains the features and predicts the thyroid nodule. Additionally, the TIRADS score classification is used for the projected malignant thyroid nodule coupled with statistical features collected from the segmented. The DBNAAF with transfer learning model is employed to classify the grading of malignant thyroid nodules, where the weights of the model are learned with transfer learning. The MCC of the Hybrid Model is 0.9445, whereas the DCNN is 0.6858, YOLOV3‐DMRF is 0.7229, CNN is 0.7780, DBN is 0.7601, Bi‐GRU is 0.7038, Deep Maxout is 0.7528, and RNN is 0.8522, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules

Gulame,

Dixit

2024

Numer Methods Biomed Eng

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“…In recent years, many methods for diagnosing and recognizing thyroid nodules based on machine learning algorithms have been proposed. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Among them, the k-nearest neighbor (KNN) algorithm, as a classic classification algorithm, has been used in many literatures for the diagnosis of thyroid nodules. Savelonas, Maroulis and Sangriotis proposed a novel computer-based approach for malignancy risk assessment of thyroid nodules in ultrasound images.…”

mentioning

confidence: 99%

“…In recent years, many methods for diagnosing and recognizing thyroid nodules based on machine learning algorithms have been proposed 5–20 . Among them, the k ‐nearest neighbor (KNN) algorithm, as a classic classification algorithm, has been used in many literatures for the diagnosis of thyroid nodules.…”

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confidence: 99%

An Improved k‐Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules

Ma,

Han,

Zhang

2024

J of Ultrasound Medicine

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ObjectivesTo complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced.MethodsAn improved k‐nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure.ResultsA total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1‐score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results.ConclusionsA comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.

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PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Saad,

Maghraby,

Badawy

2023

Neural Comput & Applic

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Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it is distinguished by abnormal cell growth in these areas. Colon polyps, which are abnormalities, can turn into cancer. To stop the spread of cancer, early polyp detection is essential. The timely removal of polyps without submitting a sample for histology is made possible by computer-assisted polyp classification. In addition to Locally Shared Features (LSF) and ensemble learning majority voting, this paper introduces a computer-aided decision support system named PolyDSS to assist endoscopists in segmenting and classifying various polyp classes using deep learning models like ResUNet and ResUNet++ and transfer learning models like EfficientNet. The PICCOLO dataset is used to train and test the PolyDSS model. To address the issue of class imbalance, data augmentation techniques were used on the dataset. To investigate the impact of each technique on the model, extensive experiments were conducted. While the classification module achieved the highest accuracy of 0.9425 by utilizing the strength of ensemble learning using majority voting, the proposed segmenting module achieved the highest Dice Similarity Coefficient (DSC) of 0.9244 using ResUNet++ and LSF. In conjunction with the Paris classification system, the PolyDSS model, with its significant results, can assist clinicians in identifying polyps early and choosing the best approach to treatment.

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A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

Cited by 6 publications

References 64 publications

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules

An Improved k‐Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

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