Automated Disease Detection from X-ray and CT Scan Images using Deep Learning

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness.

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification tasks. However, deep learning has the following problems in medical image classification. First, it is impossible to construct a deep learning model hierarchy for medical image properties; second, the network initialization weights of deep learning models are not well optimized. Therefore, this paper starts from the perspective of network optimization and improves the nonlinear modeling ability of the network through optimization methods. A new network weight initialization method is proposed, which alleviates the problem that existing deep learning model initialization is limited by the type of the nonlinear unit adopted and increases the potential of the neural network to handle different visual tasks. Moreover, through an in‐depth study of the multicolumn convolutional neural network framework, this paper finds that the number of features and the convolution kernel size at different levels of the convolutional neural network are different. In contrast, the proposed method can construct different convolutional neural network models that adapt better to the characteristics of the medical images of interest and thus can better train the resulting heterogeneous multicolumn convolutional neural networks. Finally, using the adaptive sliding window fusion mechanism proposed in this paper, both methods jointly complete the classification task of medical images. Based on the above ideas, this paper proposes a medical classification algorithm based on a weight initialization/sliding window fusion for multilevel convolutional neural networks. The methods proposed in this study were applied to breast mass, brain tumor tissue, and medical image database classification experiments. The results show that the proposed method not only achieves a higher average accuracy than that of traditional machine learning and other deep learning methods but also is more stable and more robust.

COVID-19, also known as the coronavirus disease 2019 is a highly contagious respiratory illness caused by SARS-COV-2 virus which spread form china globally resulting in pandemic. With advances in diagnostic tools, radiologic imaging is widely used for COVID-19 pneumonia diagnosis other than usual clinical and laboratory testing. In this paper, several deep learning and machine learning enhanced techniques are applied to X-Ray and CT-Scan medical images for the detection of covid-19 and also a clinical data for the prediction of the covid19. The images are preprocessed and trained using U-Net model, a popular architecture for image segmentation tasks. The accuracy and F1-score were found to be above 98% in the diagnosis of COVID-19 using CT-scan images. Further, transfer learning techniques were applied to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced CNN deep transfer learning architecture. An accuracy of 99% was achieved by enhanced CNN in the detection of X-ray images from COVID-19 and pneumonia.Further,using clinical dataset we compared the performance of supervised machine learning algorithms: logistic regression, random forest classifier, and XGBoost classifier. These algorithms were trained and evaluated on the preprocessed and feature-selected dataset to predict COVID-19 cases. The results showed that XGBoost classifier outperformed logistic regression and random forest classifier in predicting COVID-19 cases based on symptoms, age, gender, and test indications.

Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.

BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification

We previously developed two AI-based medical automatic image classification tools using a multi-layer fuzzy approach (MFA and MCM) to convert image-based abnormality into a quantity. However, there is currently limited research on using diagnostic image assessment tools to statistically predict the hazard due to the disease. The present study introduces a novel approach that addresses a substantial research gap in the identification of hazard or risk associated with a disease using an automatically quantified image-based abnormality. The method employed to ascertain hazard in an image-based quantified abnormality was the cox proportional hazard (PH) model, a unique tool in medical research for identifying hazard related to covariates. MFA was first used to quantify the abnormality in CT scan images, and hazard plots were utilized to visually represent the hazard risk over time. Hazards corresponding to image-based abnormality were then computed for the variables, ‘gender,’ ‘age,’ and ‘smoking-status’. This integrated framework potentially minimizes false negatives, identifies patients with the highest mortality risk and facilitates timely initiation of treatment. By utilizing pre-existing patient images, this method could reduce the considerable costs associated with public health research and clinical trials. Furthermore, understanding the hazard posed by widespread global diseases like COVID-19 aids medical researchers in prompt decision-making regarding treatment and preventive measures.

Artificial intelligence is being applied in various computer science domains in order to enhance the performance and decision making capability of manual frameworks, Deep learning accompanied with medical imaging is proven to be really fruitful for the detection and classification of various diseases like brain tumor, breast cancer, head and neck cancer, Hepatic and Pulmonary cancer or classification of leukemia and various diseases that can help medical professionals to diagnose and classify. Cancer is also very hard for doctors or medical professionals to spot in the very first place because major cancer symptoms are really similar to other diseases so finding cancer in a patient is a major objective especially in early stages. Pancreatic cancer, a kind of malignant neoplastic disease where the malignant neoplastic disease/malignant cells develops within the tissues of pancreas, the general survival rate of 5 year of pancreatic cancer is 10% while as the pancreas lies behind the stomach and liver so it requires intensive medical imaging. The proposed methodology presents an extensive solution for this problem, the methodology presents a deep learning based system that is able to classify pancreatic and non-pancreatic cancer through CT scan images of human pancreas. The system has been constructed using a custom VGG16 architecture using a collection of 20014 CT scan images for pancreatic and non-pancreatic cancer. The proposed methodology produces training and validation accuracy of 100%, 100% respectively.

Colorectal cancer has a high incidence rate in all countries around the world, and the survival rate of patients is improved by early detection. With the development of object detection technology based on deep learning, computer-aided diagnosis of colonoscopy medical images becomes a reality, which can effectively reduce the occurrence of missed diagnosis and misdiagnosis. In medical image recognition, the assumption that training samples follow independent identical distribution (IID) is the key to the high accuracy of deep learning. However, the classification of medical images is unbalanced in most cases. This paper proposes a new loss function named the double-balanced loss function for the deep learning model, to improve the impact of datasets on classification accuracy. It introduces the effects of sample size and sample difficulty to the loss calculation and deals with both sample size imbalance and sample difficulty imbalance. And it combines with deep learning to build the medical diagnosis model for colorectal cancer. Experimentally verified by three colorectal white-light endoscopic image datasets, the double-balanced loss function proposed in this paper has better performance on the imbalance classification problem of colorectal medical images.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists

In recent years, the integration of machine learning within the field of biological and medical sciences has significantly enhanced our ability to interpret complex clinical data. This advancement has been especially impactful in the domain of neurodegenerative disorders such as Parkinson’s Disease (PD), where early and precise diagnosis remains a persistent challenge. In this study, we propose a novel approach utilizing a Region-based Convolutional Neural Network (R-CNN) to distinguish between healthy individuals and those affected by PD. The method focuses on identifying subtle but meaningful patterns in visual diagnostic tools such as spiral and wave drawings, which are known to reflect motor impairments typical of the disease. Parkinson’s Disease is characterized by the progressive degeneration of motor control, often manifesting in tremors, rigidity, and bradykinesia. While traditional diagnostic techniques depend heavily on the observational skills of neurologists—assessing speech, writing, gait, and hand-drawn spirals—the process can be both time-consuming and subjective. To overcome these limitations, we adopted a deep learning strategy, using Faster R-CNN to directly analyze input patterns without manual feature extraction. This model automates the learning of distinguishing features from a dataset comprising MRI images and spiral drawing samples, enabling accurate classification with minimal human intervention. The system was trained and tested on real biomedical drawing datasets, including spiral and wave patterns from both PD patients and healthy controls. We achieved an accuracy of 96% during training and 86–97% during testing using batch normalization, which stabilized learning and minimized overfitting. This high performance clearly demonstrates the effectiveness of our approach in capturing the fine-grained motor distortions associated with Parkinson’s Disease. The model’s ability to learn from nuanced visual cues reflects its strength in identifying early-stage abnormalities, which are often difficult to detect with conventional tools. Moreover, this research highlights the superiority of the proposed model when compared to existing state-of-the-art methods. The results demonstrated not only robustness in pattern recognition but also the potential for real-world clinical applications. By automating a significant portion of the diagnostic workflow, this approach could reduce diagnostic delays and support neurologists in making more informed decisions. The system has proven effective in its current implementation and will be further validated using a broader, more diverse set of clinical data in future work. Keywords - Deep Learning - R-CNN - Spiral Drawing Analysis - Medical Image Classification - Early Diagnosis.

As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images that contribute to improve the accuracy of machine learning algorithm, especially for deep learning in CAD, are difficult to collect. Therefore, in this work, an improved few-shot learning method is proposed for medical image recognition. In addition, to make full use of the limited feature information in one or more samples, a feature fusion strategy is involved in our model. On the dataset of BreakHis and skin lesions, the experimental results show that our model achieved the classification accuracy of 91.22% and 71.20% respectively when only 10 labeled samples are given, which is superior to other state-of-the-art methods.

Industrial Internet of Things (IIoT) refers to the extension and utilization of the Internet of Things (IoT) in industrial sectors and applications. Medical image fusion and classification has been utilized to get valuable significant multimodality medical image data. Recently, deep learning methods offer an effective way to design an end-to-end model that can determine the final classification labels. This chapter introduces a Multimodality Image Fusion Classification (MMIFC) by the incorporation of image fusion, feature extraction, and classification techniques. Initially, the input medical images were fused by the use of optimal shearlet transform where the coefficients of shearlets are optimized by the use of Enhanced Monarch Butterfly Optimization (EMBO) algorithm. In the next stage, the fused images were classified based on the feature extractor and deep learning model to check the test image is malignant and benign. To further optimize the performance of the deep learning model, hyperparameter tuning process takes place by the use of Bayesian optimization model to optimize the weights of structure. Finally, this ODNN model classifies the MMFI as class 1 or 0. A detailed simulation result takes place to ensure the effective performance of the proposed method. From the experimental results, the proposed strategy accomplishes better fusion rate and classification results compared with other supervised and non-supervised learning techniques.

This paper addresses the task of class-imbalanced medical image classification using complication labels. Medical image classification plays an essential role in improving patient care and reducing the burden on healthcare systems, along with other computer aided diagnosis tasks. However, there are two common obstacles with medical image image classification. One is that there is a high cost of acquiring annotations from doctors/experts. Two is that in many cases there is a differences in the number of incidents that appear per symptom which lead to class-imbalance. The above factors make medical image classification difficult to deal with. Additionally, there are cases where multiple symptoms appear in a mixed manner (complications), such as diabetic complications where both symptoms of infection and ischaemia could appear at the same time, which also could influence the classification result negatively. In this paper, a method that jointly tackles the obstacles mentioned above is presented. First, we conduct extensive experiment with various class-imbalanced learning methods introduced in previous works and also propose a method that improves the baseline class-imbalanced approaches by utilizing complication labels in pre-training. Second, we validate the proposed method with the diabetic foot ulcer dataset introduced in Diabetic Foot Ulcer Challenge 2021.

Inference time correction based on confidence and uncertainty for improved deep-learning model performance and explainability in medical image classification.