Segmentation technique for medical image processing: A survey

Segmentation is one of the important and challenging tasks in medical image analysis. Region of Interest (RoI) needs to be segmented from the background in almost all types of medical images for further analysis. This assist doctors in perfect diagnosis of diseases. In this paper convolutional neural network (CNN) has been used for automatic segmentation of RoI in medical imaging modalities such as carotid artery ultrasound images. The results obtained through proposed CNN are compared with other machine learning algorithm such as support vector machine (SVM) and radial basis function (RBF). CNN exhibits an edge in performance with 10-fold cross validation over the other two networks with an accuracy of 99.3%. The results are also compared with other standard CNN architectures.

Colon cancer is cancer that is present on the inner side of colon walls or the rectum walls in the large intestine. Most of these types of cancer begin as abnormal growth of tissue called as polyp. Colonography uses low dose radiation Computed tomography (CT) scanning to obtain an interior view of the colon making use of special x-ray machine to view the large intestine for cancer and abnormal growths known as polyps. Radiologists examine these images to find polyp like structure using computer tools. As CT Colonography image contain noise such as lungs, small intestine, instruments during image capturing; segmenting colon from noise is the key task. Polyp occurrence can be detected mainly using shape feature; eliminating shapes similar to polyp is challenging. Hence, to tackle above issues, Image processing techniques are used by applying deep learning algorithm - Convolution Neural Network (CNN) and the results are compared with classical machine learning algorithm. In proposed method, each image is pre-processed to filter air filled dark region that includes colon, lungs etc. Next, each pre-processed CT Image separated into fixed number of blocks. Using pre-trained CNN, each block of ROI is classified as Type 1 (Usually Ascending and descending colon), Type 2 (Usually Traversal and sigmoidal colon) and Type3 (Noise such as lungs, instruments) to segment colon blocks by eliminating noise. Classified Blocks is further diagnosed for polyp like structure using pre-trained CNN by classifying each colon block as normal or abnormal. The experiment is setup with classical machine learning algorithms - Random Forest (RF) and k-nearest neighbor (KNN) by extracting texture feature - Local binary pattern (LBP) and shape feature - Histogram oriented gradient (HOG) for comparison. The experiment results showed the accuracy of proposed method for colon segmentation using CNN (87%) outperforms RF (85%) and KNN (83%). In, addition, the polyp detection accuracy of CNN (88%) is better than Random forest (85%) and KNN (80%). Hence, in the proposed method, there is significant accuracy improvement using deep learning algorithm compared to classical machine learning algorithms. It also provide baseline for automated colon cancer diagnosis using Deep learning algorithms for further research.

Medical image analysis plays a pivotal role in modern healthcare, aiding clinicians in accurate diagnosis and treatment planning. However, the complexity and diversity of medical images pose significant challenges for traditional image processing methods. Existing methods often struggle to precisely delineate structures in medical images, leading to suboptimal diagnostic accuracy. The demand for automated and accurate segmentation tools in medical imaging has grown, highlighting the necessity for robust and efficient algorithms capable of handling diverse anatomical variations and pathologies. While CNNs have shown promise in image analysis, their application to medical images requires customization to accommodate unique challenges. The literature lacks comprehensive studies that bridge the gap between general-purpose CNNs and the specific demands of medical image segmentation, especially concerning the diverse and intricate structures present in medical imagery. This study addresses the need for advanced techniques by leveraging Convolutional Neural Networks (CNNs) for semantic segmentation in medical image analysis. Our approach involves the design and implementation of a specialized CNN architecture tailored to the nuances of medical image data. We employ state-of-the-art techniques for data preprocessing, model training, and validation. The model is trained on a diverse dataset encompassing various medical imaging modalities, ensuring its adaptability and generalizability. The proposed CNN-based semantic segmentation model demonstrates superior performance in accurately delineating anatomical structures compared to traditional methods. Evaluation metrics, including Dice coefficient and sensitivity, indicate the model efficacy in achieving precise segmentation. The results underscore the potential of CNNs in advancing medical image analysis for improved clinical outcomes.

Shape recognition plays a significant role in the field of robot perception. In view of the low efficiency and few types of shape recognition of the fiber tactile sensor applied to flexible skin, a convolutional-neural-network-based FBG tactile sensing array shape recognition method was proposed. Firstly, a sensing array was fabricated using flexible resin and 3D printing technology. Secondly, a shape recognition system based on the tactile sensing array was constructed to collect shape data. Finally, shape classification recognition was performed using convolutional neural network, random forest, support vector machine, and k-nearest neighbor. The results indicate that the tactile sensing array exhibits good sensitivity and perception capability. The shape recognition accuracy of convolutional neural network is 96.58%, which is 6.11%, 9.44%, and 12.01% higher than that of random forest, k-nearest neighbor, and support vector machine. Its F1 is 96.95%, which is 6.3%, 8.73%, and 11.94% higher than random forest, k-nearest neighbor, and support vector machine. The research of FBG shape sensing array based on convolutional neural network provides an experimental basis for shape perception of flexible tactile sensing.

The timely diagnosis and treatment of brain diseases have always been an essential part of saving patients with encephalopathy. At the same time, medical image analysis is a significant step in diagnosing brain diseases. This article proposes a brain disease classification approach based on an improved sparrow search algorithm (ISSA). Specifically, a set of image features is first extracted to compose a pool of features for selection from five kinds of brain hemorrhage image datasets and one of the brain tumor image dataset. Second, we design an objective function to jointly reduce the number of the selected features and improve the classification accuracy. Finally, the proposed ISSA is utilized to solve the objective function. ISSA introduces three improvement mechanisms: the tent chaotic initialization, a novel local search strategy, and adaptive crossover operation to enhance the performance of conventional SSA. Moreover, a binary operator is used to solve the discrete feature selection problems. Besides, four machine learning algorithms, which are the K-nearest neighbor (KNN), support vector machine, decision tree, and random forest, are used to calculate the classification accuracy. Experimental results show that the proposed ISSA with KNN classifier eliminates about 65.9% of useless features, while the classification accuracy rate reaches more than 85%, which indicates that the proposed algorithm is more suitable for brain disease analysis than other comparison algorithms and other classifiers. Moreover, the effectiveness of the proposed improved mechanisms and the portability of different classifiers are evaluated by tests, and convolutional neural networks are compared with ISSA to classify the brain computed tomography (CT) images.

Radiological images, including X-rays, MRI, and CT scans, are essential modalities for disease diagnosis. However, the complexity of medical image data presents significant challenges in developing accurate and reliable classification models within the field of radiology. This study proposes a hybrid model that integrates an Autoencoder with a Random Forest classifier for disease classification based on radiological images. The Autoencoder is utilized to extract deep feature representations from the images, while the Random Forest serves as a robust classification algorithm. The research workflow involves preprocessing radiological image data to ensure quality and consistency, developing and training the Autoencoder to generate meaningful feature representations, and implementing the Random Forest as the classification model using the features extracted by the Autoencoder. The performance of the proposed hybrid model is evaluated by comparing it with baseline approaches, such as convolutional neural networks (CNNs) and standalone Random Forest models, using evaluation metrics including accuracy, sensitivity, and specificity, which are critical for assessing classification performance in medical imaging. This research aims to advance artificial intelligence applications in medical imaging, specifically for supporting disease diagnosis. The proposed model is anticipated to provide an efficient and reliable solution for processing radiological image data, thereby enhancing diagnostic capabilities and supporting clinical decision-making in the healthcare sector. The integration of deep feature extraction with robust classification is expected to address existing challenges in medical image analysis and contribute to the development of more effective diagnostic tools.

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.

Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017–2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.

Medical image segmentation is a fundamental task in medical image analysis, playing a crucial role in disease diagnosis, treatment planning, and clinical decision-making. Accurate segmentation of anatomical structures, such as blood vessels, organs, and lesions, is essential for reliable medical interpretations. However, existing segmentation models often face challenges in effectively capturing both local and global features within medical images. Traditional convolutional neural networks (CNNs) excel at extracting fine-grained local features but struggle to model long-range dependencies due to their limited receptive fields. Conversely, transformer-based models can capture global contextual relationships but often fail to preserve detailed local structures, leading to suboptimal segmentation performance. The purpose of this study is to develop SEFormer, a novel hybrid network that integrates SENet, ResNet, and Transformer to enhance medical image segmentation. SEFormer aims to effectively capture both local and global features, addressing the limitations of traditional CNNs and transformer-based models.SENet is incorporated to recalibrate feature maps by adaptively emphasizing informative features, thereby compensating for the global representation limitations of CNNs. Meanwhile, the ResNet backbone ensures deep feature extraction while maintaining computational efficiency. Additionally, the Transformer module is introduced to capture long-range dependencies and enhance contextual awareness, complementing the local feature extraction capabilities of CNNs. This hybrid approach allows SEFormer to balance fine-grained spatial details with broader contextual information, leading to more precise segmentation results.Furthermore, to mitigate information loss during feature extraction and ensure a more complete and hierarchical representation of image information, we introduce a feature pyramid structure inspired by multi-scale image pyramid models. By progressively increasing the receptive field across different scales and employing skip connections, SEFormer effectively fuses multi-scale local and global features at each stage of the pyramid. This hierarchical fusion mechanism ensures a richer and more robust feature representation, which is particularly beneficial for segmenting complex medical structures such as blood vessels. We evaluate SEFormer on the CHASE_DB1 dataset, a widely used benchmark for retinal vessel segmentation. Experimental results demonstrate that SEFormer outperforms existing state-of-the-art segmentation methods, achieving a 3.25% improvement in segmentation accuracy. Additionally, we conduct ablation studies to verify the contributions of different components within SEFormer. The results show that incorporating SENet enhances feature recalibration and channel attention, while the Transformer module significantly improves global context awareness. The feature pyramid structure further contributes to performance gains by ensuring a multi-scale representation of vascular structures. Compared to conventional CNN-based methods, our approach achieves better segmentation accuracy with improved robustness to variations in vessel thickness, noise, and image contrast. Furthermore, SEFormer maintains computational efficiency, making it suitable for real-world clinical applications where both accuracy and processing speed are critical. SEFormer provides an efficient and accurate approach for medical image segmentation by leveraging the complementary strengths of CNNs, SENet, and Transformers while integrating a feature pyramid structure to maximize feature representation. Future research directions include extending SEFormer to other medical imaging modalities, such as CT and MRI scans, as well as exploring further optimization techniques to reduce computational costs while maintaining high segmentation performance. Additionally, integrating SEFormer with active learning frameworks could further improve segmentation performance by leveraging expert-annotated data more efficiently.

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

Generative Adversarial Networks (GANs) are a type of neural network that can generate synthetic images that are often indistinguishable from real ones. The article explores GAN to augment existing datasets or generate new ones for training classifiers. The competitive training process of GANs results in a generator network that can produce increasingly realistic images to create more diverse and balanced datasets for training classifiers. The article discusses several successful applications of GANs in image classification, including object recognition, face classification, and medical image analysis. The datasets used in this article are CelebA and FER2013. The CelebA dataset consists of 202,599 celebrity images with 40 attributes, such as gender, age, and facial hair. The FER2013 dataset consists of 35,887 images of faces with seven other emotions, including anger, disgust, fear, happiness, sadness, surprise, and neutral. The dataset is divided into training, validation, and test sets. We resized the images to 64x64 pixels and normalized the pixel values between -1 and 1, then trained a GAN model using the dataset. We evaluate the performance of our approach and compare it with several state-of-the-art methods, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). We evaluate the performance of our approach and compare it with several state-of-the-art methods, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), with the results that our approach outperforms SVM and CNN methods on both datasets, achieving a classification accuracy of 89.2% on CelebA and 72.5% in FER2013. Meanwhile, classification accuracy on SVM was 82.3% on CelebA and 65.4% on FER2013. Classification accuracy on CNN is 87.9% on CelebA and 70.8% on FER2013.

An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

Along with the development of medical imaging technology, medical image analysis has become a crucial part of clinical diagnosis. However, traditional image analysis methods rely on manual feature extraction and expert evaluation, triggering problems such as low efficiency and lack of accuracy. Deep learning techniques, especially convolutional neural networks (CNNs), have made significant breakthroughs in medical image analysis in recent years. The aim of this paper is to study the application of deep learning in medical imaging, and explore the advantages and development potential in early disease diagnosis, image segmentation and feature extraction. This paper reviews the basic principles of deep learning and analyses the applicability of deep learning in medical imaging by combining the characteristics of medical imaging. Through systematic analysis of existing literature, this paper summarises the remarkable achievements of deep learning in processing, analysing and applying it to various medical images, such as Optical Coherence Tomography (OCT), Magnetic Resonance Imaging (MRI) and so on. It has been shown that deep learning methods can significantly improve the accuracy and efficiency of medical image processing and demonstrate performance due to traditional methods in tasks such as classification. However, how to improve the interpretability of models and the generalisability of clinical applications are still current research hotspots and challenges.

Medical image segmentation is crucial for improving healthcare outcomes. Convolutional neural networks (CNNs) have been widely applied in medical image analysis; however, their inherent inductive biases limit their ability to capture global contextual information. Vision transformer (ViT) architectures address this limitation by leveraging attention mechanisms to model global relationships; however, they typically require large-scale datasets for effective training, which is challenging in the field of medical imaging due to limited data availability. This study aimed to integrate the advantages of CNN and ViT architectures to improve segmentation performance on small-scale medical image datasets. In this study, we established a U-shaped network architecture based on a Transformer-assisted convolutional neural network (TAC-UNet). The TAC-UNet is primarily composed of a hybrid structure integrating CNN and Transformer components. Specifically, the hybrid architecture follows a dual-path design in which the Transformer branch continuously conveys global contextual information to the CNN backbone. This allows the CNN backbone to enhance its global perception while building on the local features it extracts, thereby improving its ability to comprehend complex image structures. A channel cross-attention (CCA) module is also incorporated as a bridge between the encoder and decoder to better reconcile the semantic discrepancies between them. Detailed experiments on three public datasets were conducted. Specifically, our model was trained on 30 images from the Multi-organ Nucleus Segmentation (MoNuSeg) training dataset, 85 images from the Gland Segmentation (GlaS) training dataset, and 551 images from the Computer Vision Center Colorectal Cancer-Clinic Database (CVC-ClinicDB) dataset. We evaluated the performance of our model on the corresponding test sets. Our TAC-UNet achieved the best Dice scores (80.36%, 90.70%, and 91.81% on the MoNuSeg, GlaS, and CVC-ClinicDB datasets, respectively) of all the models. Compared to other CNN-based, Transformer-based, and hybrid methods, the TAC-UNet demonstrated significantly superior segmentation performance. Our TAC-UNet model showed advanced segmentation performance on small-scale medical image datasets. The detailed experimental results showed the effectiveness of the method. Our model's code is available at: https://github.com/hejlhello/TAC-UNet.