Leukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.
This paper presents a new, simple and Efficient segmentation approach,based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable,accurate and a non-overlapped image result.The main objective of the paper is to get a non-overlapping and a reliable output by using k-means and genetic algorithm. The different colorspaces are to be fused in our application by the simple (K-means based) clustering technique on an input image. The optimized range for k-means clustering values is obtained by performing genetic algorithm. Image segmentation for six color spaces are performed by kmeans. The k-means algorithm is an iterative technique that is used to partition an image into K clusters. The obtained output remains simple to implement, fast, general enough to be applied to various computer vision applications (e.g., motion detection and segmentation). The result aims at developing an accurate and more reliable image which can be used in locating tumors, measure tissue volume, face recognition, finger print recognition and in locating an object clearly from a satellite image and in more.
Localization and navigation system are practically viable and can easily be used but are not commonly used in real time. According to world health organization there were 285 million visually impaired people and 39 million were blind. Various technologies like RFID, ZigBee have been suggested but they all possess serious drawbacks and hence are not practically used. In this paper an indoor navigation and information system that is easy to implement and use has been proposed, because it uses visible light communication (VLC), which is an ideal technology for an application like this. Visually impaired people confront unique challenges in their day-to-day life when navigating in unfamiliar public locations. The proposed system focuses on designing a device for visually impaired people that is comfortable, user friendly and also helps travelling independently. A VLC navigation system is useful for assisting blind people. This system aims to provide location information in the absence of explicit instructions and can be a supporting method for users who travel in buildings. The transmitter section contains LED lights emitting visible lights that contain positional information to provide direction to the user. In the receiver section the embedded system calculates the optimal path to a designation and speaks to the person through a headphone or speaker. In addition to the basic navigation capabilities, the proposed system also aims to provide the user certain information on the nearby points-of-interest (POI) and accessibility issues such as stairs, drain, etc. The voice or data is used as an output result of transmission of LED lights.
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