Leukocyte recognition with convolutional neural network

Accurate segmentation of leukocytes is a primary and very difficult problem because of the non-uniform color, uneven illumination of blood smear image. An improved algorithm based on feature weight adaptive K-means clustering for extracting complex leukocytes is proposed. In this paper, the initial clustering center is chosen according to the histogram distribution of a cell image; this approach not only improves the clustering effect but also reduces the time complexity of the algorithm from O (n) to O (1). Prior to white blood cell extraction, the color space is decomposed. Then, color space decomposition and K-means clustering are combined for image segmentation. And then adherent complex white blood cells are separated again based on watershed algorithm. Finally, classification experiments based on convolutional neural network were performed and compared with other methods; 368 representative images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.81% segmentation accuracy. The classification accuracy reached a maximum of 98.96%, and the average classification time is 0.39 s. Compared with those in the existing algorithms for WBC, convolutional neural network classification method not only presents obvious advantages but can also be easily improved.

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A Screen Location Method for Treating American Hyphantria cunea Larvae Using Convolutional Neural Network

Mathematical Problems in Engineering ◽

10.1155/2020/3874546 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yan Gao ◽

Ying Zhao ◽

Yujie Ji ◽

Dongjie Zhao ◽

Chong Wang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Color Space ◽

Difficult Problem ◽

Chemical Pollution ◽

Hyphantria Cunea ◽

Window Method ◽

Qualified Candidate ◽

Different Color

Chemical control is the major approach to handle the American Hyphantria cunea issue; however, it often causes chemical pollution and resource waste. How to precisely apply pesticide to reduce pollution and waste has been a difficult problem. The premise of accurate spraying of chemicals is to accurately determine the location of the spray target. In this paper, an algorithm based on a convolutional neural network (CNN) is proposed to locate the screen of American Hyphantria cunea. Specifically, comparing the effect of multicolor space-grouping convolution with that of the same color space-grouping convolution, the better effect of different color space-grouping convolution is first proved. Then, RGB and YIQ are employed to identify American Hyphantria cunea screen. Moreover, a noncoincident sliding window method is proposed to divide the image into multiple candidate boxes to reduce the number of convolutions. That is, the probability of American Hyphantria cunea is determined by grouping convolution in each candidate box, and two thresholds (E and Q) are set. When the probability is higher than E, the candidate box is regarded as excellent; when the probability is lower than Q, the candidate box is regarded as unqualified; when the probability is in between, the candidate box is regarded as qualified. The unqualified candidate box is eliminated, and the qualified candidate box cannot exit the above steps until the number of extractions of the candidate box reaches the set value or there is no qualified candidate box. Finally, all the excellent candidate boxes are fused to obtain the final recognition result. Experiments show that the recognition rate of this method is higher than 96%, and the processing time of a single picture is less than 150 ms.

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Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT

Radiation Oncology ◽

10.1186/s13014-021-01867-6 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Hideaki Hirashima ◽

Mitsuhiro Nakamura ◽

Pascal Baillehache ◽

Yusuke Fujimoto ◽

Shota Nakagawa ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Volumetric Modulated Arc Therapy ◽

Intensity Modulated Radiotherapy ◽

Deep Convolutional Neural Network ◽

Training Dataset ◽

Dice Similarity Coefficient ◽

Optimal Model ◽

Pelvic Region ◽

Segmentation Accuracy

Abstract Background This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region. Methods A total of 470 prostate cancer patients who had undergone intensity-modulated radiotherapy or volumetric-modulated arc therapy were enrolled. Our model was based on FusionNet, a fully residual deep CNN developed to semantically segment biological images. To develop the CNN-based segmentation software, 450 patients were randomly selected and separated into the training, validation and testing groups (270, 90, and 90 patients, respectively). In Experiment 1, to determine the optimal model, we first assessed the segmentation accuracy according to the size of the training dataset (90, 180, and 270 patients). In Experiment 2, the effect of varying the number of training labels on segmentation accuracy was evaluated. After determining the optimal model, in Experiment 3, the developed software was used on the remaining 20 datasets to assess the segmentation accuracy. The volumetric dice similarity coefficient (DSC) and the 95th-percentile Hausdorff distance (95%HD) were calculated to evaluate the segmentation accuracy for each organ in Experiment 3. Results In Experiment 1, the median DSC for the prostate were 0.61 for dataset 1 (90 patients), 0.86 for dataset 2 (180 patients), and 0.86 for dataset 3 (270 patients), respectively. The median DSCs for all the organs increased significantly when the number of training cases increased from 90 to 180 but did not improve upon further increase from 180 to 270. The number of labels applied during training had a little effect on the DSCs in Experiment 2. The optimal model was built by 270 patients and four organs. In Experiment 3, the median of the DSC and the 95%HD values were 0.82 and 3.23 mm for prostate; 0.71 and 3.82 mm for seminal vesicles; 0.89 and 2.65 mm for the rectum; 0.95 and 4.18 mm for the bladder, respectively. Conclusions We have developed a CNN-based segmentation software for the male pelvic region and demonstrated that the CNN-based segmentation software is efficient for the male pelvic region.

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Cellular Automata for Imaging, Art, and Video

New Constructions in Cellular Automata ◽

10.1093/oso/9780195137170.003.0015 ◽

2003 ◽

Author(s):

Joy V. Hughes

Keyword(s):

Cellular Automata ◽

State Space ◽

Color Space ◽

Three Dimensional ◽

Difficult Problem ◽

Special Effects ◽

Color Wheel ◽

Distorted Version ◽

Artistic Medium ◽

A Cell

The techniques known as Cellular Automata (CA) can be used to create a variety of visual effects. As the state space for each cell, 24-bit photo realistic color was used. Several new state transition rules were created to produce unusual and beautiful results, which can be used in an interactive program or for special effects for images or videos. This chapter presents a technique for applying CA rules to an image at several different levels of resolution and recombining the results. A “soft” artistic look can result. The concept of “targeted” CAs is introduced. A targeted CA changes the value of a cell only if it approaches a desired value using some distance metric. This technique is used to transform one image into another, to transform an image to a distorted version of itself, and to generate fractals. The author believes that the techniques presented can form the basis for a new artistic medium that is partially directed by the artist and partially emergent. Images and animations from this work are posted on the World Wide Web at (http://www.scruznet.com/~hughes/CA.html). All cellular automata (CA) operate on a space of discrete states. The simplest CAs, such as the Game of Life, use a 1-bit state space. Most modern personal computers represent color as a 24-bit value, allowing for approximately 16 million possible colors. The work presented in this chapter uses a 24-bit color space that is represented in a 32-bit-long integer. This color space can be conceptualized as a three-dimensional bounded continuous vector space. Often, it is desirable to work with in the HSV (Hue, Saturation, Value) color space. Some of the rules encode the value (luminance) of a cell in the otherwise unused 8 high-order bits of a 32-bit word. The hue and saturation can be estimated “on the fly” with simple, fast algorithms. The hue is represented as an angle on the color wheel. For some rules, it is necessary to know the “distance” between two colors. Estimating the distance in perceptual space would be a difficult problem, as it would be dependent on the monitor used and the gamma exponent applied for a particular setup.

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DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

Journal of Healthcare Engineering ◽

10.1155/2019/8597606 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Lin Teng ◽

Hang Li ◽

Shahid Karim

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Image Segmentation ◽

Convolutional Neural Network ◽

Medical Image ◽

Data Augmentation ◽

Medical Images ◽

Medical Image Segmentation ◽

Related Area ◽

Segmentation Accuracy

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.

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Detection and Classification of White Blood Cells through Deep Learning Techniques

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i15.15481 ◽

2020 ◽

Vol 16 (15) ◽

pp. 94

Author(s):

Samir Abou El-Seoud ◽

Muaad Hammuda Siala ◽

Gerard McKee

Keyword(s):

Neural Network ◽

Blood Cell ◽

Convolutional Neural Network ◽

White Blood Cell ◽

Blood Cells ◽

Human Life ◽

White Blood Cells ◽

Learning Techniques ◽

Normal White Blood Cell ◽

High Level

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.

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Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021834459 ◽

2021 ◽

Vol 8 (3) ◽

pp. 619

Author(s):

Candra Dewi ◽

Andri Santoso ◽

Indriati Indriati ◽

Nadia Artha Dewi ◽

Yoke Kusuma Arbawa

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Color Space ◽

Training Data ◽

Color Spaces ◽

Deep Learning Algorithm ◽

Optimal Accuracy

Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit diabetic retinophaty. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi diabetic retinophaty adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra fundus retina dan algoritme Convolutional Neural Network (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna. AbstractIncreasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.

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Identifikasi Motif Jepara pada Ukiran dengan Memanfaatkan Convolutional Neural Network

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) ◽

10.22146/jnteti.v9i4.541 ◽

2020 ◽

Vol 9 (4) ◽

pp. 403-413

Author(s):

Sandhopi ◽

Lukman Zaman P.C.S.W ◽

Yosi Kristian

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Color Space ◽

Sliding Window ◽

Heat Maps

Semakin berkembang motif ukiran, semakin beragam bentuk dan variasinya. Hal ini menyulitkan dalam menentukan suatu ukiran bermotif Jepara. Pada makalah ini, metode transfer learning dengan FC yang dikembangkan dimanfaatkan untuk mengidentifikasi motif khas Jepara pada suatu ukiran. Dataset dibedakan menjadi tiga color space, yaitu LUV, RGB, dan YcrCb. Selain itu, sliding window, non-max suppression, dan heat maps dimanfaatkan untuk proses penelusuran area objek ukiran dan pengidentifikasian motif Jepara. Hasil pengujian dari semua bobot menunjukkan bahwa Xception pada klasifikasi motif Jepara memiliki nilai akurasi tertinggi, yaitu 0,95, 0,95, dan 0,94 untuk masing-masing dataset color space LUV, RGB, dan YCrCb. Namun, ketika semua bobot model tersebut diterapkan pada sistem identifikasi motif Jepara, ResNet50 mampu mengungguli semua jaringan dengan nilai persentase identifikasi motif sebesar 84%, 79%, dan 80%, untuk masing-masing color space LUV, RGB, dan YCrCb. Hasil ini membuktikan bahwa sistem mampu membantu dalam proses menentukan suatu ukiran, termasuk ke dalam ukiran Jepara atau bukan, dengan mengidentifikasi motif-motif khas Jepara yang terdapat dalam ukiran.

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Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames

The Journal of Defense Modeling and Simulation Applications Methodology Technology ◽

10.1177/1548512920954542 ◽

2020 ◽

pp. 154851292095454

Author(s):

Junfeng Zhang ◽

Qing Xue

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Decision Making ◽

Reinforcement Learning ◽

Convolutional Neural Network ◽

Difficult Problem ◽

Learning Method ◽

Rule Based ◽

Autonomous Decision ◽

Decision Making Problem

In a tactical wargame, the decisions of the artificial intelligence (AI) commander are critical to the final combat result. Due to the existence of fog-of-war, AI commanders are faced with unknown and invisible information on the battlefield and lack of understanding of the situation, and it is difficult to make appropriate tactical strategies. The traditional knowledge rule-based decision-making method lacks flexibility and autonomy. How to make flexible and autonomous decision-making when facing complex battlefield situations is a difficult problem. This paper aims to solve the decision-making problem of the AI commander by using the deep reinforcement learning (DRL) method. We develop a tactical wargame as the research environment, which contains built-in script AI and supports the machine–machine combat mode. On this basis, an end-to-end actor–critic framework for commander decision making based on the convolutional neural network is designed to represent the battlefield situation and the reinforcement learning method is used to try different tactical strategies. Finally, we carry out a combat experiment between a DRL-based agent and a rule-based agent in a jungle terrain scenario. The result shows that the AI commander who adopts the actor–critic method successfully learns how to get a higher score in the tactical wargame, and the DRL-based agent has a higher winning ratio than the rule-based agent.

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SoC FPGA Accelerated Sub-Optimized Binary Fully Convolutional Neural Network for Robotic Floor Region Segmentation

Sensors ◽

10.3390/s20216133 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6133

Author(s):

Chi-Chia Sun ◽

Afaroj Ahamad ◽

Pin-He Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Heterogeneous Computing ◽

Robot Vision ◽

Route Planning ◽

Indoor Environments ◽

Embedded Devices ◽

Efficient Reduction ◽

Segmentation Accuracy ◽

Binarization Method

In this article, a new Binary Fully Convolutional Neural Network (B-FCN) based on Taguchi method sub-optimization for the segmentation of robotic floor regions, which can precisely distinguish floor regions in complex indoor environments is proposed. This methodology is quite suitable for robot vision in an embedded platform and the segmentation accuracy is up to 84.80% on average. A total of 6000 training datasets were used to improve the accuracy and reach convergence. On the other hand, to reach real-time computation, a PYNQ FPGA platform with heterogeneous computing acceleration was used to accelerate the proposed B-FCN architecture. Overall, robots would benefit from better navigation and route planning in our approach. The FPGA synthesis of our binarization method indicates an efficient reduction in the BRAM size to 0.5–1% and also GOPS/W is sufficiently high. Notably, the proposed faster architecture is ideal for low power embedded devices that need to solve the shortest path problem, path searching, and motion planning.

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IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

Journal of Healthcare Engineering ◽

10.1155/2020/6648574 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Nighat Bibi ◽

Misba Sikandar ◽

Ikram Ud Din ◽

Ahmad Almogren ◽

Sikandar Ali

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Early Stage ◽

Learning Algorithms ◽

White Blood Cells ◽

Machine Learning Algorithms ◽

Network Resources ◽

And Performance

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

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