Classification of breast cancer grades using physical parameters and K-nearest neighbor method

Breast cancer is a health problem in the world. To overcome this problem requires early detection of breast cancer. The purpose of this study is to classify early breast cancer grades. Combination of physical parameters with k-nearest neighbor Method is proposed to detect early breast cancer grades. The experiments were performed on 87 mammograms consisting of 12 mammograms of grade 1,41 mammograms of grade 2 and 34 mammogram of grade 3. The proposed method was effective to classify the grades of breast cancer by an accuracy of 64.36%, 50% sensitivity and 73,5% specitifity. Physical parameters can be used to classify grades of breast cancer. The results of this study can be used to complement the diagnosis of breast mammography examination

The Utilization of Physics Parameter to Classify Histopathology Types of Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) by using K-Nearest Neighbourhood (KNN) Method

Medical imaging process has evolved since 1996 until now. The forming of Computer Aided Diagnostic (CAD) is very helpful to the radiologists to diagnose breast cancer. KNN method is a method to do classification toward the object based on the learning data which the range is nearest to the object. We analysed two types of cancers IDC dan ILC. 10 parameters were observed in 1-10 pixels distance in 145 IDC dan 7 ILC. We found that the Mean of Hm(yd,d) at 1-5 pixeis the only significant parameters that distingguish IDC and ILC. This parameter at 1-5 pixels should be applied in KNN method. This finding need to be tested in diffrerent areas before it will be applied in cancer diagnostic

Pengolahan citra mammografi cara pembuatan program

xiv, 247 hlm.: 26 c

Belajar fisika menggunakan logika

x, 109 p. ; 23 cm

Teori dan praktik prinsip digital

Buku ini menjelaskan tentang Sistem Bilangan dan Gerbang Logika Dasar, Mintern, Maxterm dan Karnaugh Map, Arithmatic : Half Adder Dan Full Adder, Arithmatic : Half Subtraktor Dan Full Subtraktor, Decoder, Multiplekser, Rangkaian Sekuensial, Register. Software yang digunakan adalah Proteus.xiv, 91 hlm.: 24 c

Statistika parametik, non parametik dan multivariant

x, 272 hlm. ; 24 c

Pengolahan Citra Mammografi

Buku ini disusun berdasarkan kebutuhan para pembaca di bidang Teknik Informatika, Ilmu Komputer, Fisika, dan Radiologi. Buku Pengolahan Citra Mammografi ini merupakan buku yang bersifat tutorial sehingga dalam pembahasannya setiap babnya disertai contoh-contoh aplikasi. Buku ini menjelaskan tentang Open Image, Histogram Equlization, Wiener Filter, Global Thresholding, Morfologi, Substrak, Deteksi Tepi Sobel, Deteksi Tepi Prewitt, Deteksi Tepi Robert, Low Pass Filter, High Pass Filter, K-Mean, dan Log In. Buku ini diharapkan dapat membantu pembaca di bidang Teknik Informatika, Ilmu Komputer, Fisika, dan Radiologi, yang sedang melakukan penelitian

Can the Physical Parameters with the Support Vector Machine (SVM) Method Able to Classify Benign and Malignant Breast Cancer?

Objective: Evaluating the diagnostic performance of SVM to classify benign and malignant by performing a meta-analysis. Methods: The data used for this study were secondary data. It consisted of 221 mammogram images (mean age 57.5 years) with 164 malignant and 57 benign, taken from a radiological database that has been examined by a radiologist with more than 20 years of experience. Also, histopathological record data that had been examined by an oncologist with more than 20 years of experience. Mammograms were taken from January 2022 to June 2022. In all, 221 mammograms consisting of 164 malignant and 57 benign were used as SVM method training, and 20 mammograms consisting of 10 malignant and 10 benign were used to test the performance of the SVM method. It was then evaluated using pathology results as the gold standard. Results: Benign had a significantly lower deviation (an average of 29.2661230 ± 10.14916673) than malignant (an average of 33.1841234 ± 11.70238757). The SVM method performance value obtained the values ​​of TP, FP, TN, FN, accuracy, sensitivity, Specificity, and Precision, respectively 7,7, 3, 3, 50%, 70%, 30%, and 50%. Conclusion: A proper performance to distinguish benign and malignant can be obtained using the physical deviation parameters with the SVM classification approach. However, these findings should be proven in larger datasets with different mammographic scanners. Our meta-analysis shows that the physical parameters and SVM have high sensitivity but low specificity. Of the nine physical parameters in the mammogram, only the parameter deviation was significant to distinguish between benign and malignant. The SVM method proved to be able to differentiate between benign and malignant

Sistem Pendeteksi Banjir Berbasis Sensor Ultrasonik Hc-Sr04 Dan Modul Esp8266-12e Dengan Media Komunikasi Telegram Dan Buzzer

It has been designed the water surface level detection system based on the ultrasonic sensor HC-SR04 and the ESP8266-12E module through telegram and  buzzer  communication  media..  The  research  aims  to  design  a    water surface-level detection system by using ultrasonic sensors and the ESP8266-12E  module. The tools and materials used  during the design  are:   Ultrasonic sensors HC-SR04, module ESP8266-12E, and buzzer as an output to sound the alarm. The ultrasonic HC-SR04 sensor will detect the water surface level,    the detection data will be sent to the ESP8266-12E module, then the system will send the information in the form of a message through telegram and buzzer application.  The  messages  delivered  are  several  stages  including  standby, alert,  and  danger.  The  methods  used  in  this  design  are  planning,     study libraries,  collection  of  tools  and  materials,  hardware  plan,  and    program creation on the software. The final result is a system capable of detecting water surface level based on the ultrasonic sensor  HC-SR04 and the    ESP8266-12E module through telegram and buzzer communication media. The success    rate of this tool system at several stages among others at a standby of 77%, at a level of alert of 70%, and the level of success at the hazard level is 83%.

Combination of Modified Soybean Tempeh M-3 with Carrot Increases the Total Blood Antioxidant Capacity, Decreases 8-Hydroxy-2-Deoxyguanocine, and Skin Texture Damage in Rat

distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The combination of tempeh M-3 with carrots is one of the functional foods which contain bioactive compounds in the form of antioxidant that has a function as an agent scavenger to free radical caused oxidative stress following to the exposure of ultraviolet ray. The existence of radicals can increase the level of 8-hydroxy-2dioksiguanosin and also can damage the skin texture.The aim of the research was to show the effect of combination of tempeh M-3 with carrots supplementation that can increasing blood total antioxidant capacity, reducing level 8-hydroxy-2deoksiguanosin and skin texture damage caused ultraviolet radiation. This study was designed as the randomized post test only control group design with independent variable are (Po) 0gram tempeh M-3 and carrot. P1:1gram Tempeh-3 and carrot. P2:gram, 2gram, and 3 gram /kg BW/day. 50 Sri Wahjuni and Anak Agung Ngurah Gunawan The Result showed that there was an increase of blood total antioxidan