Design of microscopic image diagnosis system basedon MVC model and J2EE platform

An esophageal cancer intelligent diagnosis system is developed to improve the recognition rate of esophageal cancer image diagnosis and the efficiency of physicians, as well as to improve the level of esophageal cancer image diagnosis in primary care institutions. In this paper, by collecting medical images related to esophageal cancer over the years, we establish an intelligent diagnosis system based on the convolutional neural network for esophageal cancer images through the steps of data annotation, image preprocessing, data enhancement, and deep learning to assist doctors in intelligent diagnosis. The convolutional neural network-based esophageal cancer image intelligent diagnosis system has been successfully applied in hospitals and widely praised by frontline doctors. This system is beneficial for primary care physicians to improve the overall accuracy of esophageal cancer diagnosis and reduce the risk of death of esophageal cancer patients. We also analyze that the efficacy of radiation therapy for esophageal cancer can be influenced by many factors, and clinical attention should be paid to grasp the relevant factors in order to improve the final treatment effect and prognosis of patients.

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Microscopic Image Recognition-based Fish Disease Diagnosis System

Journal of Convergence Information Technology ◽

10.4156/jcit.vol6.issue10.45 ◽

2011 ◽

Vol 6 (10) ◽

pp. 355-364 ◽

Cited By ~ 1

Author(s):

SangWoong Lee ◽

JeongSeon Park

Keyword(s):

Image Recognition ◽

Disease Diagnosis ◽

Microscopic Image ◽

Fish Disease ◽

Diagnosis System

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Deteksi Parasit Plasmodium pada Citra Mikroskopis Hapusan Darah dengan Metode Deep Learning

ELKOMIKA Jurnal Teknik Energi Elektrik Teknik Telekomunikasi & Teknik Elektronika ◽

10.26760/elkomika.v9i2.306 ◽

2021 ◽

Vol 9 (2) ◽

pp. 306

Author(s):

NOR KUMALASARI CAECAR PRATIWI ◽

NUR IBRAHIM ◽

YUNENDAH NUR FU’ADAH ◽

SYAMSUL RIZAL

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Anopheles Mosquito ◽

Microscopic Image ◽

Test Results ◽

Automatic Diagnosis ◽

Diagnosis System ◽

Slowing Down ◽

The World

ABSTRAKParasit plasmodium merupakan makhluk protozoa bersel satu yang menjadi penyebab penyakit malaria. Plasmodium ini dibawa melalui gigitan nyamuk anopheles betina. Dalam World Malaria Report 2015 menyatakan bahwa malaria telah menyerang sedikit 106 negara di dunia. Di Indonesia sendiri, Papua, NTT dan Maluku merupakan wilayah dengan kasus positif malaria tertinggi. Malaria telah menjadi masalah yang serius, sehingga keberadaan sistem diagnosa otomatis yang cepat dan handal sangat diperlukan untuk proses perlambatan penyeberan dan pembasmian epidemi. Dalam penelitian ini akan dirancang sistem yang mampu mendeteksi parasit malaria pada citra mikroskopis darah menggunakan arsitekur Convolutional Neural Network (CNN) sederhana. Hasil pengujian menunjukkan bahwa metode yang diusulkan memberikan presisi dan recall sebesar 0,98 dan f1-score sebesar 0,96 serta akurasi 95,83%.Kata kunci: parasit, malaria, convolutional neural network, citra mikroskopis ABSTRACTPlasmodium parasites are single-celled protozoan creatures that cause malaria. Plasmodium is carried through the bite of a female Anopheles mosquito. The World Malaria Report 2015 states that malaria has attacked at least 106 countries in the world. In Indonesia itself, Papua, NTT and Maluku are the regions with the highest positive cases of malaria. Malaria has become a serious problem, so the existence of a fast and reliable automatic diagnosis system is indispensable for the process of slowing down the spread and eliminating the epidemic. In this study, a system capable of detecting malaria parasites in microscopic images of blood will be designed using a simple Convolutional Neural Network (CNN) architecture. The test results show that the proposed method provides precision and recall of 0,98, f1-values of 0.96 and accuracy of 95,83%.Keywords: parasites, malaria, convolutional neural network, microscopic image

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