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Histopatolojik görüntülerde derin öğrenme temelli öğreticisiz doku bölütlemesi
In the current practice of medicine, histopathological examination of tissues is essential for cancer diagnosis. However, this task is both subject to observer variability and time consuming for pathologists. Thus, it is important to develop automated objective tools, the first step of which usually comprises image segmentation. According to this need, in this thesis, we propose a novel approach for the segmentation of histopathological tissue images. Our proposed method, called deepSeg, is a two-tier method. The first tier transfers the knowledge from AlexNet, which is a convolutional neural network (CNN) trained for the non-medical domain of ImageNet, to the medical domain of histopathological tissue image characterization. The second tier uses this characterization in a seed-controlled region growing algorithm, for the unsupervised segmentation of heterogeneous tissue images into their homogeneous regions. To test the effectiveness of the segmentation, we conduct experiments on microscopic colon tissue images. Quantitative results reveal that the proposed method improves the performance of the previous methods that work on the same dataset. This study both illustrates one of the first successful demonstrations of using deep learning for tissue image segmentation, and shows the power of using deep learning features instead of handcrafted ones in the domain of histopathological image analysis.
Araç konum tahmini ve araç sınıflandırması derin evrimsel sinir ağları kullanarak
The aim of this master's thesis is to classify the vehicles and estimate the position with license plate localization using Deep Convolutional Neural Network (DCNN). Vehicle pose estimation with license plate localization serves as one of the most widely-used real-world applications in fields like toll control, traffic scene analysis, and suspected vehicle tracking. Along with license plate information, to obtain overall comprehension, the information of the owner vehicle also plays a great role, and contextual information is defined as the relationship between the vehicles pose license plate and the owner vehicle in our work. We proposed a one-stage anchor-free vehicle classifier for simultaneously localizing the region of license plates and vehicles' poses. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle pose estimation with license plates localization. For single scale input, we reached mean Precision Accuracy mAP/mAP50 of 35.4/82.3 on the Laboratory for Intelligence and Safe Automobile (LISA) benchmark dataset, already outperformed the existing commercial systems OpenALPR and Sighthound. For multi-scale input, we reached the best mAP/mAP50 of 40.8/90.1. For the vehicle pose (front-rear), classification accuracy reached 98.8%, average IoU reached 71.3%, giving a promising result as an end-to-end vehicle position estimation and license plate localization with contextual information. The work has performed in python programming language with several libraries of deep learning were being used for this purpose. There are three functional head networks in our design, and thus the end-to-end and simultaneous training leads to a potential training instability. If the model fails to converge, it is quite hard to trace which head design is raising a problem. Thus, in our design process, we added each the functional head step-by-step, making sure the single head design idea is working correctly and then expanded the model with the rest functional heads. Our DCNN model training started from an initial weight which we had already trained for about 110000 iterations in the model without classification head, so the total training iterations will be around 780000 including the transfer learning part in DCNN. Transfer learning made the DCNN model start at a smart point and made it easier to optimize all of the functional heads simultaneously. Keywords: Vehicle classification, pose estimation, optimization, DCNN, transfer learning, license plate, localization, deep learning.
Israrlı ortam erişim kontrol protokolü tasarlama ve hizmet kalitesinde enerji bilinçli yönlendirme garantili kablosuz vücut alanı ağı
Wireless body area network (WBAN) is a type of wireless sensor network that enables efficient healthcare systems. To minimize frequent sensor replacement due to resource restrictions, it is necessary to improve energy efficiency in WBAN. This thesis deals with energy efficiency and QoS improvement together in novel WBAN architecture. A novel WBAN architecture is designed with dual sink nodes in order to minimize delay and energy consumption. A novel insistence aware medium access control (IA-MAC) protocol which is aware of criticality of sensed data is presented in proposed WBAN. Prior-knowledge based weighted routing (PWR) algorithm is responsible to select optimal route for data transmission. In PWR, weight value is computed by considering significant metrics such as residual energy, link stability, distance, delay, etc. in order to improve energy efficiency and QoS in the network. Energy consumption is further minimized by incorporating graph based sleep scheduling (GSS) algorithm. In GSS, criticality of sensor node also considered as major metric. In coordinator, split and map based neural network (SMNN) classifier is involved to perform packet classification. After classification, packets are assigned to corresponding sink node accordance to packet type. Then, throughput and delay metrics are improved by frame aggregation process which is involved in sink node. Extensive simulation in OMNeT++ shows better performance in network lifetime, throughput, residual energy, dropped packets, and delay.