Bone Cancer Detection Research Articles

BCDNet: A Deep Learning Model with Improved Convolutional Neural Network for Efficient Detection of Bone Cancer Using Histology Images

Among the several types of cancer, bone cancer is the most lethal prevailing in the world. Its prevention is better than cure. Besides early detection of bone cancer has potential to have medical intervention to prevent spread of malignant cells and help patients to recover from the disease. Many medical imaging modalities such as histology, histopathology, radiology, X-rays, MRIs, CT scans, phototherapy, PET and ultrasounds are being used in bone cancer detection research. However, hematoxylin and eosin stained histology images are found crucial for early diagnosis of bone cancer. Existing Convolutional Neural Network (CNN) based deep learning techniques are found suitable for medical image analytics. However, the models are prone to mediocre performance unless configured properly with empirical study. Within this article, we suggested a framework centered on deep learning for automatic bone cancer detection. We also proposed a CNN variant known as Bone Cancer Detection Network (BCDNet) which is configured and optimized for detection of a common kind of bone cancer named Osteosarcoma. An algorithm known as Learning based Osteosarcoma Detection (LbOD). It exploits BCDNet model for both binomial and multi-class classification. Osteosarcoma-Tumor-Assessment is the histology dataset used for our empirical study. Our the outcomes of the trial showed that BCDNet outperforms baseline models with 96.29% accuracy in binary classification and 94.69% accuracy in multi-class classification.

Detection of bone cancer based on a four-phase framework generative deep belief neural network in deep learning

The medical image processing plays an important role in analyzing and detecting tumors and detecting cancer cells in their early stages. Microscopic images were used as a deep learning approach to recognize bone features. These images were acquired using microscopy, a time-consuming and labor-intensive process that required repeating. Cancer cells cannot be identified by conventional techniques because of the noise in the images. To address these challenges, this paper introduces the Gated Convolutional Neural Network (GCNN) techniques used to identify multiclass bone cancer, including non-tumor, non-viable, and viable tumors. As a first stage, the most basic part of image Preprocessing is to deposing without interrupting the diagnostics information during the removal of noise. The second stage is edge detection, used for image brightness discontinuity detection based on Scaled Region Edge Detector (SRED). It is used for image segmentation and data extraction within regions based on the Screen Cluster Area Segmentation (SCAS) to segment the Region of Interest (RoI) based on Relative Scalar Color Histogram (RSCH). The fourth step is Feature extraction is to find out the features and match the image feature from the Bone Cancer Detection object based on the maximum threshold weights using GCNN with Maximum Standard External Regions (MSER) used for features extracting applied to improve feature quality analysis and Feature support rate. This results in higher classification accuracy, Precision, recall under F-measure, and lower error rates. This enables a more achieved classification than other existing methods.

Detecting bone lesions in X-ray under diverse acquisition conditions.

The diagnosis of primary bone tumors is challenging as the initial complaints are often non-specific. The early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed. We propose an automatic algorithm to detect bone lesions in conventional radiographs to facilitate early diagnosis. Detecting lesions in such radiographs is challenging. First, the prevalence of bone cancer is very low; any method must show high precision to avoid a prohibitive number of false alarms. Second, radiographs taken in health maintenance organizations (HMOs) or emergency departments (EDs) suffer from inherent diversity due to different X-ray machines, technicians, and imaging protocols. This diversity poses a major challenge to any automatic analysis method. We propose training an off-the-shelf object detection algorithm to detect lesions in radiographs. The novelty of our approach stems from a dedicated preprocessing stage that directly addresses the diversity of the data. The preprocessing consists of self-supervised region-of-interest detection using vision transformer (ViT), and a foreground-based histogram equalization for contrast enhancement to relevant regions only. We evaluate our method via a retrospective study that analyzes bone tumors on radiographs acquired from January 2003 to December 2018 under diverse acquisition protocols. Our method obtains 82.43% sensitivity at a 1.5% false-positive rate and surpasses existing preprocessing methods. For lesion detection, our method achieves 82.5% accuracy and an IoU of 0.69. The proposed preprocessing method enables effectively coping with the inherent diversity of radiographs acquired in HMOs and EDs.

An attention-based bidirectional long short-term memory based optimal deep learning technique for bone cancer detection and classifications

An attention-based bidirectional long short-term memory based optimal deep learning technique for bone cancer detection and classifications

Feature Extraction Based Machine Learning Approach for Bone Cancer Detection

Osteosarcoma is a type of cancer that develops in the bones. Though it can happen in any bone, it commonly happens in long bones like the legs and arms. As a result, early detection and categorization of bone cancers have become critical for treating patients. A wavelet-based segmentation algorithm was utilized in this work to detect bone cancers. The segmented bone cancer components were then processed further for categorization. The enhanced convolutional neural network (ECNN) classification was employed in this investigation to differentiate between benign and malignant bone cancers. Collect multiple photos and use wavelet transform features to extract a trained classifier model. Sensitivity (97%), Specificity (97%), Precision (98%), Accuracy (97.5%), and F1Score (97.5) are the performance metrics for the ECNN deep learning (DL) algorithm. According to the results, ECNN deep learning beats deep learning methods, including SVM, ANN, and RNN. As a result, the ECNN deep learning technology can be used to diagnose bone cancer more accurately. Based on histology pictures, our enhanced model performs at the cutting edge of detecting osteosarcoma cancer.

Radiolabeling, Quality Control, and Cell Binding Studies of New 99mTc-Labeled Bisphosphonates: 99mTc-Ibandronate Sodium.

Early detection of bone cancer is critical for treating symptoms, minimizing pain, and increasing overall quality of life. It is critical to develop novel radiopharmaceuticals with high labeling efficiency and stability for the diagnosis of bone cancer. This research aims to design a novel radiopharmaceutical that may be used to diagnose bone cancer. In this study, ibandronate sodium (IBD), a bisphosphonate analog, was radiolabeled with technetium-99m [99mTc] and quality control tests on the newly developed radiopharmaceutical ([99mTc]Tc-IBD) were performed using radioactive thin layer chromatography. After that, the incorporation of [99mTc]Tc-IBD into hydroxyapatite (HA) crystals and a human bone osteosarcoma cell line (U2OS) was tested. According to the results obtained, optimal radiolabeling procedure was obtained for [99mTc]Tc-IBD with 200 μg.mL-1 IBD, 20 μg stannous chloride, and 99mTc with 37 MBq radioactivity. The reaction mixture was adjusted to pH 5.5 and incubated at room temperature for 15 min. The radiochemical purity of [99mTc]Tc-IBD was found to be greater than 95% at room temperature for up to 6 h. Additionally, chromatography analysis showed >95% [99mTc]Tc-IBD complex formation with promising stability for up to 24 h in saline and up to 2 h in cell medium. The percentage binding of IBD to HA was 83.70 ± 3.67 and the logP of [99mTc]Tc-IBD was -1.1014. The radiolabeled complex exhibited a higher rate of cell incorporation to U2OS cells compared to Reduced/Hydrolyzed 99mTcO4 -. The newly produced radiopharmaceutical is very promising according to the results of in vitro cell culture, HA binding, and quality studies, and will be a step forward for further studies in nuclear medicine for bone cancer diagnostics.

Application of the convolutional neural networks and supervised deep-learning methods for osteosarcoma bone cancer detection

Osteosarcoma is a cancerous tumor that occurs in bones. Although it can occur in any bone, it often occurs in long bones such as arms and legs. The exact cause of this cancerous tumor is still unknown, but according to experts, it occurs due to the deoxyribonucleic acid (DNA) mutations inside the bones. This creates immature, irregular, diseased bone and can destroy healthy body tissue. About 75 out of 100 people who have osteosarcoma can be cured if the cancer is not dispersed to the additional body parts. The bone X-ray is the initial test when a bone tumor is suspected. X-ray and imaging tests are the best way to identify osteosarcoma from the bones. A biopsy is the suggested method that can make a definitive diagnosis. This is a time-consuming and difficult procedure that can be automated. We propose several supervised deep-learning methods and select the most suitable model. The selection is made through the weightage from the users’ data to detect bone cancer. We show the model selected meets the expectations with the highest accuracy 90.36% using the residual neural network(ResNet101) algorithm and 89.51% precision in the prediction tasks.

DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction

Among the many different types of cancer, bone cancer is the most lethal and least prevalent. More cases are reported each year. Early diagnosis of bone cancer is crucial since it helps limit the spread of malignant cells and reduce mortality. The manual method of detection of bone cancer is cumbersome and requires specialized knowledge. A deep transfer-based bone cancer diagnosis (DTBV) system using VGG16 feature extraction is proposed to address these issues. The proposed DTBV system uses a transfer learning (TL) approach in which a pre-trained convolutional neural network (CNN) model is used to extract features from the pre-processed input image and a support vector machine (SVM) model is used to train using these features to distinguish between cancerous and healthy bone. The CNN is applied to the image datasets as it provides better image recognition with high accuracy when the layers in neural network feature extraction increase. In the proposed DTBV system, the VGG16 model extracts the features from the input X-ray image. A mutual information statistic that measures the dependency between the different features is then used to select the best features. This is the first time this method has been used for detecting bone cancer. Once selected features are selected, they are fed into the SVM classifier. The SVM model classifies the given testing dataset into malignant and benign categories. A comprehensive performance evaluation has demonstrated that the proposed DTBV system is highly efficient in detecting bone cancer, with an accuracy of 93.9%, which is more accurate than other existing systems.

Bone Cancer Detection and Classification Using Owl Search Algorithm With Deep Learning on X-Ray Images

Bone Cancer Detection and Classification Using Owl Search Algorithm With Deep Learning on X-Ray Images

Bone Cancer Detection from X-Ray and MRI Images through Image Segmentation Techniques

This paper is based on integration of the biomedical field and computer science. Paper contains the study of bone cancer and features to predict the type of the same. Related work to find cancer in human body using computer vision is discussed in this paper. Image segmentation technique like sobel, prewitt, canny, K-means and Region Growing are described in this paper which can be stimulated for X-Ray and MRI image interpretation. Paper also shows the result of edge based and region based image segmentation techniques applied on X-Ray image to detect osteosarcoma cancer present on bone using MATLAB. Finally, paper concluded by finding best suited segmentation method for grey scaled image with future aspects.

RETRACTED ARTICLE: Fuzzy rank correlation-based segmentation method and deep neural network for bone cancer identification

Bone malignant tumors are one of the important health problems because tumors are formed due to the affectedness of the healthiest bone tissues. This serious bone cancer has been identified with the help of the different risk factors such as chills, swear symptoms, swelling, bones weaken risk, and night swears symptoms. These symptoms are not easy to predict in beginning stage with accurate manner. So, automatic bone cancer detection system has been developed to predict the cancer in earlier sate. Initially, the bone images are collected from patient, and noise in the images is eliminated using median filter. After eliminating the noise, affected tumor part is detected by applying the intuitionistic fuzzy rank correlation. From the detected intuitionistic fuzzy-based clustered images, different statistical features are extracted. The derived features are processed by applying the deep neural networks layers that successfully examines each features using Levenberg–Marquardt learning algorithm. The successful learning process predicts bone cancer-related features with accurate manner (99.1%). Finally, excellence of bone cancer prediction system is analyzed using MATLAB-based experimental setup and performance metrics such as F1 score, accuracy, error rate and so on.

Bioinformatics Image Based Decision Support System for Bone Cancer Detection

Bioinformatics Image Based Decision Support System for Bone Cancer Detection

A novel method on earlier detection of bone cancer using Markov random field segmentation

An enchondroma is a benign tumour found in the bone marrow and it accounts for up to 10-25% of people with benign tumours. This paper proposes an approach to detect enchondroma tumour through Magnetic Resonance (MR) images. The proposed method includes pre-processing technique such as Gaussian filter in order to remove noise and smooth images. Segmentation is the main technique which gives precise knowledge about the image using the Markov random filed approach. The feature extraction and naive Bayes classifier works effectively and detects the tumour cells which are affected by enchondroma. The proposed method works efficiently and also detects the presence of enchondroma in the MR images.

A novel method on earlier detection of bone cancer using Markov random field segmentation

An enchondroma is a benign tumour found in the bone marrow and it accounts for up to 10-25% of people with benign tumours. This paper proposes an approach to detect enchondroma tumour through Magnetic Resonance (MR) images. The proposed method includes pre-processing technique such as Gaussian filter in order to remove noise and smooth images. Segmentation is the main technique which gives precise knowledge about the image using the Markov random filed approach. The feature extraction and naive Bayes classifier works effectively and detects the tumour cells which are affected by enchondroma. The proposed method works efficiently and also detects the presence of enchondroma in the MR images.

DESIGN OF CANTILEVER BASED BIOSENSOR FOR BONE CANCER DETECTION

This paper present the design of cantilever-based biosensors which proves to bean interesting research tool that is fast and cheap for the detection of bone cancer. In cantilever based biosensor, adevice dimension changes the sensitivity of the device reduces which is of prime important in biosensor. Micro Electro Mechanical System is constructed to achieve a certain engineering function by the integration of mechanical elements, sensors, actuators and electronics on a common silicon substrate through micro-fabrication technology.The cantilever will help in the early detection of bone cancer. The deflections in the cantilever caused by the stress and strain in the cantilever are studied for the detection of the bone cancer.