Breast cancer is one of the most occurring cancers in women due to the uncontrolled growth of abnormal cells in the lobules or milk ducts. The treatment for the breast cancer at an early stage is important using Magnetic Resonance Imaging (MRI) which effectively measures the size of the cancer and also checks tumors in the opposite breast. The deposition of calcium components on the breast tissue is known as micro-calcifications. The calcium salts deposited in the breast are involved with the cancer and were not diagnosed accurately due to the low effectiveness of existing imaging technique namely Haralick feature extraction technique. The MRI breast cancer diagnosis creates problems during classification of breast image and leads to misclassifications, such as unidentified calcium deposits in the existing K-Nearest Neighbour (KNN) classifier. The misclassification issues are overcome by an accurate classification and identification of calcium salts and checks whether deposited salt on breast tissue is involved with cancer or not. Initially, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used to remove the unwanted noise in the MRI and Morphological, Multilevel Otsu’s Thresholding and region growing techniques perform segmentation to mask unwanted breast tissues. The proposed Hybrid LOOP Haralick feature extraction technique is developed by combining the both Local Optimal Oriented Pattern (LOOP) and Haralick texture feature and the hybrid parameters are applied to the Stacked Auto Encoder based (SAE) to classify the breast MRI image as a Malignant or Benign. The performance of the proposed hybrid LOOP Haralick feature extraction shows significant accuracy improvement of 3.83% when compared to the Haralick feature extraction technique.
In recent years, speech processing has become an active research area in the field of signal processing due to the usage of automated systems for spoken language interface. In developed countries, the customer service with automated system in speech synthesis has been the recent trend. The existing automated speech synthesis systems have certain problems during the real time implementation such as lack of naturalness in output speech, lack of emotions and so on. In this study, the novel Text to Speech system is introduced along with the sentiment analysis in Tamil language. The input text is first classified into the positive, negative and neutral based on the emotions in the sentence then the text is converted into speech with emotions during TTS conversion. Existing approaches used neural network based classifiers for classification. But, neural networks have certain drawbacks in real time training. So, this research study uses Fuzzy Neural Network (FNN) to classify the sentence based on the emotions. The text to speech with sentiment analysis effective scheme which is evaluated using Doordarshan news Tamil dataset. The proposed scheme is implemented using MATLAB. This TTS system has several social applications, especially in railway stations where the announcements can be made through expressive speech.
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