Deformation Expression of Soft Tissue Based on BP Neural Network

Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of several objects like hair, reflections, air bubbles, and oils on almost all images. Active contour is one of the suitable methods with some drawbacks for the segmentation of irregular shapes. An entropy and morphology-based automated mask selection is proposed for the active contour method. The proposed method can improve the overall segmentation along with the boundary of melanoma images. In this study, features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP). When four different moments pull out in six different color spaces like HSV, Lin RGB, YIQ, YCbCr, XYZ, and CIE L*a*b then global information from different colors channels have been combined. Therefore, hybrid fused texture features; such as local, color feature as global, shape features, and Artificial neural network (ANN) as classifiers have been proposed for the categorization of the malignant and non-malignant. Experimentations had been carried out on datasets Dermis, DermQuest, and PH2. The results of our advanced method showed superiority and contrast with the existing state-of-the-art techniques.

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“…The shape also improves performance when combined with texture and color features. Shape features have been extracted from segmented images [32].…”

Section: Discussion and Limitationmentioning

confidence: 99%

Hybrid Color Texture Features Classification Through ANN for Melanoma

Mustafa¹,

Jaffar²,

Iqbal³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Further improvements of landmarks matching could be sought through the use of the matching results for the malformation quantification and preoperative planning. The framework could also take advantage form the machine learning [42], neural networks [43] and optimized deep architecture [44].…”

Section: Anomalies Classification Using the Svm And Knnmentioning

confidence: 99%

Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

Ismail¹,

Elmannai²,

Meshoul³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity information. Then, the maxillofacial anomalies were classified using the Support Vector Machine (SVM). The experiments were conducted on X-ray cephalograms of 400 patients where the ground truth was produced by two experts. The frameworks achieved a landmark matching error (LE) of 0.50 ± 1.04 and a successful landmark matching of 89.47% in the 2 mm and 3 mm range and of 100% in the 4 mm range. The classification of anomalies achieved an accuracy of 98.75%. Compared to previous work, the proposed approach is simpler and has a comparable range of acceptable matching cost and anomaly classification. Results have also shown that it outperformed the K-nearest neighbors (KNN) classifier.

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“…x worst,ci = x min + (x max − x min + 1) × rand (9) Now x max and x min indicate the upper and lower limits, correspondingly. x worst,ci describes the lesser individual elephant in clan ci.…”

Section: Parameter Optimizationmentioning

confidence: 99%

“…Pain in various regions might portend skeletal metastasis; thus, it needs to be properly examined [8]. With regards to disease diagnosis, the normal investigation methods for the assessment of possible osteosarcoma include laboratory tests, X-ray of the infected bone, chest Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI) scans of the infected bone, whole-body technetium bone scan, percutaneous image-guided biopsy and chest X-ray [9]. Even though biopsy-based method identifies the malignancy in an efficient manner, the limitation of histological-guided biopsies and MRI scans has constrained the detection ability.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

Malibari¹,

Obayya²,

Negm³

et al. 2022

Computers, Materials &Amp; Continua

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Osteosarcoma is a type of malignant bone tumor that is reported across the globe. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the detection and classification of malignancies in biomedical images. In this regard, the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning (BOIC-EHODTL) model. The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma. At the initial stage, Gabor Filter (GF) is applied as a pre-processing technique to get rid of the noise from images. In addition, Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors. Then, EHO algorithm is utilized along with Adaptive Neuro-Fuzzy Classifier (ANFC) model for recognition and categorization of osteosarcoma. EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results. The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study. In order to demonstrate the improved performance of BOIC-EHODTL model, a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

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Deformation Expression of Soft Tissue Based on BP Neural Network

Cited by 101 publications

References 14 publications

Hybrid Color Texture Features Classification Through ANN for Melanoma

Hybrid Color Texture Features Classification Through ANN for Melanoma

Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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