Bone Cancer Cell Prediction Using an Enhanced Deep Learning Algorithm with an Optimization Technique

<abstract> <p>Bone cancer detection is an essential region of medical analysis but developments in medical imaging and artificial intelligence (AI) are vital. Using approaches, namely deep learning (DL) and machine learning (ML), radiologists and medical staff can examine X-ray, CT, and MRI scans to identify bone cancer and abnormalities. These technologies support earlier diagnosis, correct diagnosis, and treatment planning, enhancing patient solutions. The combination of AI-driven image analysis and the knowledge of medical practitioners improves the speed and precision of bone cancer detection, contributing to more effectual clinical activities. DL algorithms, particularly CNNs, are exposed to great performance in image classification tasks and are extremely utilized for medical image analysis. We offer a Hybrid Rice Optimization Algorithm with DL-Assisted Bone Cancer Detection (HROADL-BCD) technique on medical X-ray images. The major intention of the HROADL-BCD method is to examine the X-ray images for the recognition of bone cancer. In the presented HROADL-BCD method, a bilateral filtering (BF) process was performed to remove the noise. To derive feature vectors, the HROADL-BCD technique applied the EfficientNet model. The HROADL-BCD technique involved the HROA for hyperparameter tuning of the EfficientNet model. Last, the bone cancer detection and classification process were executed by the attention-based bidirectional long short-term memory (ABiLSTM) approach. A wide range of simulations could be applied for the simulation result analysis of the HROADL-BCD algorithm. The extensive outcome of the HROADL-BCD approach inferred the superior outcome of 97.62% outcome concerning various aspects.</p> </abstract>

Bone cancer detection from X-ray images is challenging due to subtle tumor characteristics, radiologist fatigue, and limited access to specialized expertise, particularly in resource-constrained settings. Early and accurate diagnosis is critical for improving patient outcomes. However, conventional methods rely heavily on manual interpretation, leading to delays and variability. This study proposes a web-integrated AI system for automated bone cancer prediction using deep learning. The system employs the EfficientNet-B0 architecture with transfer learning to classify bone X-ray images as normal or cancerous, supported by preprocessing, data augmentation, and class-weighting techniques to address data scarcity and class imbalance. The trained model is deployed as a user-friendly web application enabling X-ray upload, report generation, scan history management, and interaction through an integrated AI chatbot. The proposed system aims to provide an efficient, interpretable, and clinically supportive diagnostic tool for early bone cancer detection.

Bone cancer is a significant health issue that leads to severe complications and deaths worldwide. Early detection can significantly improve patient outcomes, often resulting in a complete cure. Traditional approaches to managing bone cancer can be augmented with technology-driven methods, particularly those enabled by artificial intelligence. Convolutional Neural Networks (CNNs) and their variants have proven effective in medical data analysis. However, deep learning techniques require enhancements to improve performance in cancer detection across various medical imaging modalities. In this paper, we propose a deep-learning framework that utilizes advanced CNN models for the automatic screening of bone cancer. We have enhanced both the CNN model and the ResNet-50 model, which are integral components of the proposed framework. Additionally, we introduced an algorithm called Learning-based Bone Cancer Detection (LbBCD), designed to optimize the utilization of these enhanced deep learning models to improve bone cancer detection efficiency. Our research emphasizes a Region of Interest (ROI) based approach to enhance the screening process for bone cancer. Using a benchmark dataset known as the Bone CT Scan dataset, our empirical study demonstrated that the proposed deep learning framework, integrated with enhanced CNN and ResNet-50 models, achieved remarkable performance. Specifically, the enhanced CNN model reached an accuracy of 90.90%, while the enhanced ResNet-50 model achieved an accuracy of 92.20%, outperforming state-of-the-art deep learning models. Therefore, this proposed system can be integrated into healthcare applications for automatically screening bone cancer, contributing to a clinical decision support system for healthcare professionals.

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.

Cancer is a dangerous disease, which is caused because of unregulated cell growth. After many researches, almost 100 different types of cancer has been detected in human body. Out of these, one of the most widely spread is bone cancer, which leads to death. The detection of bone cancer is very critical and which has no anticipation. Presently, most of the study is done by using data mining methods and the image processing techniques for medical image analysis process. The data and the knowledge collecting from large databases and related websites have been predictable by many scientific researchers. Association rule mining, supports vector machines, fuzzy theory and probabilistic neural networks and learning vector quantization are the mostly used methods for detection and classification of bone cancer. This paper used k means clustering algorithm for bone image segmentation. The segmented image is further processed for bone cancer detection by evaluating the mean intensity the identified area. Threshold values are proposed for the classification of medical images for the presence or absence of bone cancer. This method uses jpeg images, but also applicable for original format of DICOM (digital imaging communication of medicine) medical images if any modifications are done. The results using this method gives 95% accuracy with less computational time.

Bone cancer is treated as a severe health problem, and, in many cases, it causes patient death. Early detection of bone cancer is efficient in reducing the spread of malignant cells and decreasing mortality. Since the manual detection process is a laborious task, it is needed to design an automated system to classify and identify the cancerous bone and the healthy bone. Therefore, this article develops an Owl Search Algorithm with a Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) technique. The OSADL-BCDC algorithm follows the principle of transfer learning with a hyperparameter tuning strategy for bone cancer detection. The OSADL-BCDC model employs Inception v3 as a pretrained model for the feature extraction process which does not necessitate a manual segmentation of X-ray images. Besides, the OSA is applied as a hyperparameter optimizer for enhancing the efficacy of the Inception v3 method. Finally, the long short-term memory (LSTM) approach is used for identifying the presence of bone cancer. The proposed OSADL-BCDC technique reduces diagnosis time and achieves faster convergence. The experimental analysis of the OSADL-BCDC algorithm is tested using a set of medical images and the outcomes were measured under different aspects. The comparison study highlighted the improved performance of the OSADL-BCDC model over existing algorithms.

Diagnosis of bone cancer using histopathology images is essential for effective and timely treatment. However, contemporary diagnostic methods struggle to achieve high accuracy and interpretability while utilizing computational methods. Although existing methodologies in deep learning are promising, each suffers from significant limitations that arise from fundamental challenges in hyperparameter optimization, explainability, and generalizability across disparate datasets. Such disadvantages serve as barriers to clinical use, underscoring the need for a more reliable and comprehensible diagnostic framework. In this study, an Optimized Deep Learning Framework for Bone Cancer Detection (ODLF-BCD) algorithm is proposed by jointly combining Enhanced Bayesian Optimization (EBO), deep transfer learning from state-of-the-art pre-trained models (i.e., EfficientNet-B4, ResNet50, DenseNet121, InceptionV3, and VGG16), and explainable artificial intelligence, namely Grad-CAM and SHAP. It mitigates the state-of-the-art limitations through hyperparameter tuning, increased transparency, and data augmentation to balance the dataset. Extensive experiments verify the effectiveness of the proposed framework, where EfficientNet-B4 achieves 97.9% and 97.3% for binary and multi-class classification, respectively. Its performance is also confirmed with high precision, recall, and F1 score. Explainability facilitates the clinical interpretability of model predictions. Then, the proposed framework offers a robust and efficient alternative solution to the C-RAD, automating bone cancer diagnosis and enhancing the accuracy and transparency of the diagnosis. Its potential usefulness could provide clinicians with strong decision support systems for early and precise cancer detection.

Canine bone cancer is a common type of cancer that grows fast and may be fatal. It usually appears in the limbs which is called appendicular bone Diagnostic imaging methods such as X-rays, computed tomography (CT scan), and magnetic resonance imaging (MRI) are more common methods in bone cancer detection than invasive physical examination such as biopsy. These imaging methods have some disadvantages; including high expense, high dose of radiation, and keeping the patient (canine) motionless during the imaging procedures. This project study identifies the possibility of using thermographic images as a pre-screening tool for diagnosis of bone cancer in dogs. Experiments were performed with thermographic images from 40 dogs exhibiting the disease bone cancer. Experiments were performed with color normalization using temperature data provided by the Long Island Veterinary Specialists. The images were first divided into four groups according to body parts (Elbow/Knee, Full Limb, Shoulder/Hip and Wrist). Each of the groups was then further divided into three sub-groups according to views (Anterior, Lateral and Posterior). Thermographic pattern of normal and abnormal dogs were analyzed using feature extraction and pattern classification tools. Texture features, spectral feature and histogram features were extracted from the thermograms and were used for pattern classification. The best classification success rate in canine bone cancer detection is 90% with sensitivity of 100% and specificity of 80% produced by anterior view of full-limb region with nearest neighbor classification method and normRGB-lum color normalization method. Our results show that it is possible to use thermographic imaging as a pre-screening tool for detection of canine bone cancer.

Abstract—Bone cancer is a serious health condition that often leads to high mortality rates. Early detection plays a crucial role in limiting the spread of cancerous cells and improving patient outcomes. However, manual detection is a time-consuming and labor-intensive process, necessitating th6+e development of an automated system to efficiently classify and identify cancerous and healthy bone tissues. This paper introduces the Owl Search Algorithm with Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) model, which combines transfer learning and hyperparameter optimization for effective bone cancer detection. The OSADL-BCDC approach utilizes the Inception v3 model as a pretrained feature extractor, eliminating the need for manual image segmentation. The Owl Search Algorithm (OSA) is employed as a hyperparameter optimizer to enhance the performance of Inception v3. Additionally, Long Short-Term Memory (LSTM) networks are leveraged to detect the presence of bone cancer. The proposed OSADL-BCDC method reduces diagnosis time and achieves faster convergence, demonstrating superior performance in experimental evaluations compared to existing algorithms. The results of the comparison emphasize the effectiveness and improved accuracy of the OSADL-BCDC model for bone cancer detection. Keywords—Bone cancer, Owl search, hyperparameter tuning, Inception v3

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Chapter 17 - Bone cancer detection using machine learning techniques

Bone cancer is one of the prevalent cancers, any fracture or abnormalities (Cancer) need to be predicted at early stage like other cancers because it spreads to the other parts of body or organs is known as metastasis. Bone cancer is also known as Osteosarcoma, which is malignant primitive bone tumor. Bone cancer is of two types primary and secondary where primary bone cancer originates from Bone and spreads to the other parts of body. On the other hand Secondary bone cancer is also called bone metastasis it occurs when cancerous cell spreads from other parts of body towards bone. This paper presents the different types of Bone cancer such as bone sarcoma and bone metastasis and their stages. As different acquisition techniques plays vital role in the detection of the bone cancer or any type of cancer so this work also discussed different image acquisition techniques. Secondly, this work discussed different classification techniques and their comparison for bone cancer detection. This paper discussed regarding the work done in the field of Bone cancer detection using different feature selection, segmentation and classification techniques on different types of images such as MRI, X-ray and CT Scan and also discussed the challenges and opportunities for Bone cancer detection.

Bone cancer, also known as bone sarcoma, is a rare cancer that grows abnormal tissue in bones. This malignancy is highly likely to metastasize. Because of this, early classification and detection of bone cancer are now the most essential variables in predicting a patient's cure. An adaptive fuzzy clustering by local approximation of mEmbership (AFLAME) was developed as a method for investigating a potential strategy for identifying bone cancer in this body of work. For a wide variety of applications, accurate classification and segmentation of bone tumors are absolutely necessary steps. However, getting there has been tough because many methods, like medical imaging techniques, don't have enough non-homogeneous and contrast intensity to accomplish the goal. This makes progress toward the objective more challenging. Support vector machine (SVM) classifiers are used to complete the classification process. In this study, we provide a new method for segmenting bone cancer, opening up new avenues of inquiry into this important topic.

Among the many types of cancers, bone cancer is one with which most of the deaths occur in the world. Around 10000 deaths are occurring in a year in India due to bone cancer. Bone cancer is the most dangerous and deaths can be avoided if it is detected in the early stage. Here, an automatic bone cancer detection system is proposed to aid the oncologists in early detection of bone cancers and helps them to undergo a timely treatment. Support Vector Machine (SVM) based M3 filtered and Fuzzy C-Means (FCM) segmentation method is proposed to detect the bone cancers. An accuracy of about 92% is achieved with the proposed method.

The malignant cells that cannot be controlled from spreading throughout the body is Cancer. Among which the cancer occurs in bone is their type. It is malignant disease occur in bone of human body where their growth cant be controlled from growing. This bone cancer is very critical of all the cancer types since the malignant cells are not identified at their earlier stage and it is the major challenge. Bone cancer is highly common for children and teenagers. For earlier detection of this cancer the correlation of medical imaging has been adapted with image processing and machine learning techniques where maximum accuracy can be obtained similarly even for bone cancer. This paper proposes the detection of bone cancer from the dataset taken from clinical dataset. Here the proposed design comprises of 2 phases in predicting the disorder with higher accuracy. The first stage is extracting the feature of segmented bone image using Gray-Level Co-occurrence Matrix (GLCM) method is applied to extract the features in terms of statistical texture-based and the second phase is classification of extracted feature using K-NN with decision tree algorithm. The simulation results show the enhanced classification results and extracted output with higher accuracy.