- MeSH
- Electronic Data Processing MeSH
- Diagnosis, Computer-Assisted MeSH
- Electroencephalography MeSH
- Publication type
- Comparative Study MeSH
Background: Breast cancer is one of the leading cancers in woman worldwide both in developed and developing nations as per the records from World Health Organization. Many studies have shown that mammography is very effective tool for the breast cancer diagnosis. Mass segmentation plays an important step for the cancer detection. Objective: The objective of the proposed method is to segment the mass and to classify the mass with high accuracy. Methods: The segmentation includes two main steps. First, a rough initial segmentation through iterative thresholding, and second, an active contour based segmentation. The relevant statistical features are extracted and the classification is done by using Adaptive Neuro Fuzzy Inference System (ANFIS). Results: The proposed mass detection scheme achieves sensitivity of 87.5% and specificity of 100% for a set of twenty two images. The overall segmentation accuracy obtained is 91.30%. Conclusions: This work appears to be of high clinical significance since the mass detection plays an important role in diagnosis of breast cancer.
- MeSH
- Electroencephalography methods MeSH
- Epilepsy physiopathology therapy MeSH
- Anesthesia, Intravenous classification MeSH
- Humans MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
A new approach to visual evaluation of long-term EEG recordings is proposed. The method is based on multichannel adaptive segmentation, subsequent feature extraction, automatic classification of the acquired segments by fuzzy cluster analysis (fuzzy c-means algorithm), and on the distinguishing of thus identified EEG segments by colour directly in the EEG record. The black and white variant of the described automatic system is presented. The method was evaluated by applying it to simulated artificial data and to real EEG recordings; some of the illustrative results are shown. In addition, the performance of this system is evaluated and the first experience with its application to routine EEG recordings is discussed.
Punctuational theories of evolution suggest that adaptive evolution proceeds mostly, or even entirely, in the distinct periods of existence of a particular species. The mechanisms of this punctuated nature of evolution suggested by the various theories differ. Therefore the predictions of particular theories concerning various evolutionary phenomena also differ.Punctuational theories can be subdivided into five classes, which differ in their mechanism and their evolutionary and ecological implications. For example, the transilience model of Templeton (class III), genetic revolution model of Mayr (class IV) or the frozen plasticity theory of Flegr (class V), suggests that adaptive evolution in sexual species is operative shortly after the emergence of a species by peripatric speciation--while it is evolutionary plastic. To a major degree, i.e. throughout 98-99% of their existence, sexual species are evolutionarily frozen (class III) or elastic (class IV and V) on a microevolutionary time scale and evolutionarily frozen on a macroevolutionary time scale and can only wait for extinction, or the highly improbable return of a population segment to the plastic state due to peripatric speciation.The punctuational theories have many evolutionary and ecological implications. Most of these predictions could be tested empirically, and should be analyzed in greater depth theoretically. The punctuational theories offer many new predictions that need to be tested, but also provide explanations for a much broader spectrum of known biological phenomena than classical gradualistic evolutionary theories.
Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP-SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.
- Publication type
- Journal Article MeSH
Identifying the presence and extent of early ischemic changes (EIC) on Non-Contrast Computed Tomography (NCCT) is key to diagnosing and making time-sensitive treatment decisions in patients that present with Acute Ischemic Stroke (AIS). Segmenting EIC on NCCT is however a challenging task. In this study, we investigated a 3D CNN based on nnU-Net, a self-adapting CNN technique that has become the state-of-the-art in medical image segmentation, for segmenting EIC in NCCT of AIS patients. We trained and tested this model on a sizeable and heterogenous dataset of 534 patients, split into 438 for training and validation and 96 for testing. On this test set, we additionally assessed the inter-rater performance by comparing the proposed approach against two reference segmentation annotations by expert neuroradiologist readers, using this as the benchmark against which to compare our model. In terms of spatial agreement, we report median Dice Similarity Coefficients (DSCs) of 39.8% for the model vs. Reader-1, 39.4% for the model vs. Reader-2, and 55.6% for Reader-2 vs. Reader-1. In terms of lesion volume agreement, we report Intraclass Correlation Coefficients (ICCs) of 83.4% for model vs. Reader-1, 80.4% for model vs. Reader-2, and 94.8% for Reader-2 vs. Reader-1. Based on these results, we conclude that our model performs well relative to expert human performance and therefore may be useful as a decision-aid for clinicians.
- MeSH
- Renal Insufficiency, Chronic MeSH
- Glomerulosclerosis, Focal Segmental MeSH
- Kidney Glomerulus physiopathology pathology MeSH
- Hypertension MeSH
- Rats MeSH
- Kidney physiopathology pathology MeSH
- Disease Models, Animal MeSH
- Nephrectomy MeSH
- Disease Progression MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Animals MeSH
- Publication type
- Review MeSH
