CN111640097A - 皮肤镜图像识别方法及设备 - Google Patents
皮肤镜图像识别方法及设备 Download PDFInfo
- Publication number
- CN111640097A CN111640097A CN202010455685.4A CN202010455685A CN111640097A CN 111640097 A CN111640097 A CN 111640097A CN 202010455685 A CN202010455685 A CN 202010455685A CN 111640097 A CN111640097 A CN 111640097A
- Authority
- CN
- China
- Prior art keywords
- images
- dermatoscope
- difference
- recognition result
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000013528 artificial neural network Methods 0.000 claims abstract description 57
- 238000000605 extraction Methods 0.000 claims abstract description 33
- 201000001441 melanoma Diseases 0.000 claims abstract description 29
- 238000010801 machine learning Methods 0.000 claims abstract description 15
- 238000012549 training Methods 0.000 claims description 55
- 230000006870 function Effects 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 13
- 238000010606 normalization Methods 0.000 claims description 10
- 238000012935 Averaging Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 10
- 230000003902 lesion Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000002405 diagnostic procedure Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 231100000444 skin lesion Toxicity 0.000 description 2
- 206010040882 skin lesion Diseases 0.000 description 2
- PCTMTFRHKVHKIS-BMFZQQSSSA-N (1s,3r,4e,6e,8e,10e,12e,14e,16e,18s,19r,20r,21s,25r,27r,30r,31r,33s,35r,37s,38r)-3-[(2r,3s,4s,5s,6r)-4-amino-3,5-dihydroxy-6-methyloxan-2-yl]oxy-19,25,27,30,31,33,35,37-octahydroxy-18,20,21-trimethyl-23-oxo-22,39-dioxabicyclo[33.3.1]nonatriaconta-4,6,8,10 Chemical compound C1C=C2C[C@@H](OS(O)(=O)=O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2.O[C@H]1[C@@H](N)[C@H](O)[C@@H](C)O[C@H]1O[C@H]1/C=C/C=C/C=C/C=C/C=C/C=C/C=C/[C@H](C)[C@@H](O)[C@@H](C)[C@H](C)OC(=O)C[C@H](O)C[C@H](O)CC[C@@H](O)[C@H](O)C[C@H](O)C[C@](O)(C[C@H](O)[C@H]2C(O)=O)O[C@H]2C1 PCTMTFRHKVHKIS-BMFZQQSSSA-N 0.000 description 1
- 208000000453 Skin Neoplasms Diseases 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011176 pooling Methods 0.000 description 1
- 201000000849 skin cancer Diseases 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20224—Image subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30088—Skin; Dermal
Landscapes
- Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Image Processing (AREA)
Abstract
Description
Claims (15)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010455685.4A CN111640097B (zh) | 2020-05-26 | 2020-05-26 | 皮肤镜图像识别方法及设备 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010455685.4A CN111640097B (zh) | 2020-05-26 | 2020-05-26 | 皮肤镜图像识别方法及设备 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111640097A true CN111640097A (zh) | 2020-09-08 |
CN111640097B CN111640097B (zh) | 2023-10-17 |
Family
ID=72331487
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010455685.4A Active CN111640097B (zh) | 2020-05-26 | 2020-05-26 | 皮肤镜图像识别方法及设备 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111640097B (zh) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112785562A (zh) * | 2021-01-13 | 2021-05-11 | 北京智拓视界科技有限责任公司 | 一种基于神经网络模型进行评估的系统和相关产品 |
CN112884706A (zh) * | 2021-01-13 | 2021-06-01 | 北京智拓视界科技有限责任公司 | 一种基于神经网络模型的图像评估系统和相关产品 |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20170287134A1 (en) * | 2016-03-31 | 2017-10-05 | International Business Machines Corporation | Annotation of skin image using learned feature |
CN107909566A (zh) * | 2017-10-28 | 2018-04-13 | 杭州电子科技大学 | 一种基于深度学习的皮肤癌黑色素瘤的图像识别方法 |
CN110852396A (zh) * | 2019-11-15 | 2020-02-28 | 苏州中科华影健康科技有限公司 | 一种宫颈图像的样本数据处理方法 |
-
2020
- 2020-05-26 CN CN202010455685.4A patent/CN111640097B/zh active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20170287134A1 (en) * | 2016-03-31 | 2017-10-05 | International Business Machines Corporation | Annotation of skin image using learned feature |
CN107909566A (zh) * | 2017-10-28 | 2018-04-13 | 杭州电子科技大学 | 一种基于深度学习的皮肤癌黑色素瘤的图像识别方法 |
CN110852396A (zh) * | 2019-11-15 | 2020-02-28 | 苏州中科华影健康科技有限公司 | 一种宫颈图像的样本数据处理方法 |
Non-Patent Citations (1)
Title |
---|
李航;余镇;倪东;雷柏英;汪天富;: "基于深度残差网络的皮肤镜图像黑色素瘤的识别" * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112785562A (zh) * | 2021-01-13 | 2021-05-11 | 北京智拓视界科技有限责任公司 | 一种基于神经网络模型进行评估的系统和相关产品 |
CN112884706A (zh) * | 2021-01-13 | 2021-06-01 | 北京智拓视界科技有限责任公司 | 一种基于神经网络模型的图像评估系统和相关产品 |
Also Published As
Publication number | Publication date |
---|---|
CN111640097B (zh) | 2023-10-17 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cerentini et al. | Automatic identification of glaucoma using deep learning methods | |
US8315440B2 (en) | System and method for animal identification using iris images | |
Rajan et al. | Diagnosis of cardiovascular diseases using retinal images through vessel segmentation graph | |
US20210133473A1 (en) | Learning apparatus and learning method | |
CN111553436A (zh) | 训练数据生成方法、模型训练方法及设备 | |
CN111563910B (zh) | 眼底图像分割方法及设备 | |
CN111640097B (zh) | 皮肤镜图像识别方法及设备 | |
Bourbakis | Detecting abnormal patterns in WCE images | |
CN106530294A (zh) | 一种对睑板腺图像进行处理以获得腺体参数的信息的方法 | |
Hernandez-Ortega et al. | A comparative evaluation of heart rate estimation methods using face videos | |
CN115969369A (zh) | 一种大脑任务负荷识别方法、应用及设备 | |
Putra et al. | Retracted: Identification of Heart Disease With Iridology Using Backpropagation Neural Network | |
Marusina et al. | Automatic analysis of medical images based on fractal methods | |
CN111640127B (zh) | 一种用于骨科的精准临床诊断导航方法 | |
CN112741651A (zh) | 一种内窥镜超声影像的处理方法及系统 | |
Putri et al. | Retracted: Implementation of Neural Network Classification for Diabetes Mellitus Prediction System through Iridology Image | |
CN112330603B (zh) | 基于软组织表面形变估计组织内部目标运动的系统与方法 | |
CN115239695A (zh) | 一种基于时序图像的肺结节识别系统及方法 | |
CN111640126B (zh) | 基于医学影像的人工智能诊断辅助方法 | |
Agafonova et al. | Meningioma detection in MR images using convolutional neural network and computer vision methods | |
CN109816632B (zh) | 脑图像处理方法、装置、可读存储介质及电子设备 | |
Carnimeo et al. | Retinal vessel extraction by a combined neural network–wavelet enhancement method | |
Anggraeni et al. | Detection of the emergence of exudate on the image of retina using extreme learning machine method | |
Paul et al. | Computer-Aided Diagnosis Using Hybrid Technique for Fastened and Accurate Analysis of Tuberculosis Detection with Adaboost and Learning Vector Quantization | |
Relan et al. | Robustness of Fourier fractal analysis in differentiating subgroups of retinal images |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Yu Zhen Inventor after: Cheng Xuelian Inventor after: Ju Lie Inventor after: Wang Xin Inventor after: Xiong Jianhao Inventor after: Ge Zongyuan Inventor after: Zhao Xin Inventor after: He Chao Inventor after: Zhang Dalei Inventor before: Yu Zhen Inventor before: Chen Xuelian Inventor before: Ju Lie Inventor before: Wang Xin Inventor before: Xiong Jianhao Inventor before: Ge Zongyuan Inventor before: Zhao Xin Inventor before: He Chao Inventor before: Zhang Dalei |