Multimodal Brain Tumor Segmentation using Neighboring Image Features

Brain tumor can grow anywhere in the brain with irregular contours and appearance. It is very hard to correctly segment the tumor tissues due to the similarity, noise, complex texture, poor sampling and image distortions. In this article, an enhanced novel technique for brain tumor detection is introduced by using multimodal (T1, T2, T1c, Flair) MR images. The proposed method consists of two main steps. In the first step, supervised binomial classification method is used to classify MR images into tumorous and non-tumorous by extracting Discrete Cosine Transform (DCT) features and applying k-nearest neighbors (KNN) classifier. In the second step, segmented the tumor by manipulating image intensity values and used neighboring image features along with the actual image features. We further enhanced the tumor segmentation by applying region-growing algorithm. The proposed method is tested on MICCAI BraTS 2015, a wellknown standard dataset. Receiver Operating Characteristic (ROC), Dice Similarity Coefficient (DSC) and Mutual Information (MI) are used to measure the performance and achieved 96.91% accuracy for the binomial classification and 93.22% accuracy for the tumor segmentation

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Introduction Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. Method This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. Results The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. Conclusion In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.© 2023 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Impulse Noise Removal Using Soft-computing

Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation

Improving Brain MR Image Classification for Tumor Segmentation using Phase Congruency

MRI which stands for Magnetic Resonance Imaging is commonly used to capture images of internal body organs, functionality and structure. Manual analysis is usually performed by Radiologists on a large set of MR images in order to detect brain tumor. Aims: This research aims to improve automated brain MR image classification and tumor segmentation using phase congruency. Methods: The skull part is removed from brain MR image by applying converging square algorithm and phase congruency based edge detection method. Features are then extracted from the segmented brain portion using discrete wavelet transforms. In order to minimize the extracted feature set, we applied the principal Component Analysis algorithm. The MR images are classified into tumorous and non-tumorous using Multilayer perceptron and compared with other classifiers such as K-Nearest Neighbor, Naïve Bayes, and Support Vector Machines (SVM) along with discrete cosine and discrete cosine transform features. The tumor is segmented using Fuzzy C-mean and reconstructed tumor 3D model to measure the volume, location and shape accurately. Results & conclusions: Experimental results are obtained by testing the proposed method on a dataset of 19 patients with a total number of 2920 brain MR images. The proposed method achieved an accuracy of 99.43% for classification which is higher as compared to other current studies. Keywords: Brain MRI, phase congruency, segmentation, tumor analysis, feature extraction, tumor classificatio

Cross-Cultural Emotion Classification based on Incremental Learning and LBP-Features

A number of studies have shown that facial expression representations are cultural dependent and not universal. Most facial expression recognition (FER) systems use one or two datasets for training and same for testing and show good results. While their performance mortify radically when datasets from different cultures were presented. To keep high accuracy for a long time and for all cultures, a FER system should learn incrementally. We proposed a FER system that can offer incremental learning capability. Local Binary Pattern (LBP) Features are used for Region of Interest (ROI) extraction and classification. We used static images of facial expressions from different cultures for training and testing. The experiments on five different datasets using the incremental learning classification demonstrate promising results

A modified adaptive differential evolution algorithm for color image segmentation

Image segmentation is an important low-level vision task. It is a perceptual grouping of pixels based on some similarity criteria. In this paper, a new differential evolution (DE) algorithm, modified adaptive differential evolution, is proposed for color image segmentation. The DE/current-to-pbest mutation strategy with optional external archive and opposition-based learning are used to diversify the search space and expedite the convergence process. Control parameters are automatically updated to appropriate values in order to avoid user intervention of parameters setting. To find an optimal number of clusters (the number of regions or segments), the average ratio of fuzzy overlap and fuzzy separation is used as a cluster validity index. The results demonstrate that the proposed technique outperforms state-of-the-art methods

Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network

Mammograms can be used to check for breast cancer in women. In this paper, we have proposed breast cancer detection into two stages. In the first stage, mammograms have to classify into malignant and benign. While in second stage, the type of abnormality is detected. Features have been extracted using Discrete Wavelet Transform. These wavelet based features has been reduced using Principle Component Analysis. Those images which have been classified as malignant in the first stage are further classified into six classes to check its abnormality. It has been observed that the accuracy of classification of abnormalities is more than 90%. Mammographic Institute Society Analysis dataset is used for experimentation