Identification In Advance Breast Cancer Detection Using The Embedded System With Iot Technology And Mlx90614 Temperature Sensor

DOI : 10.17577/IJERTCONV11IS07012

Download Full-Text PDF Cite this Publication

Text Only Version

Identification In Advance Breast Cancer Detection Using The Embedded System With Iot Technology And Mlx90614 Temperature Sensor

Ms. M.S. Yoga Priya

Assistant Professsor, Department of BME, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur,

Tamilnadu, India

R. Haritha UG Scholar

Department of BME, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamilnadu, India

R. Elavarasi UG Scholar

Department of BME, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamilnadu, India


Breast cancer is one of the toughest survival challenges in our life. Nowadays, in Indian women are highly flown by majorly in breast cancer. This is the second most perilous cancer in our world. Mammograms are the primary detecting device used all over the world, however, because of the air gaps, they havent been as successful. Since 2018, proton therapy is a therapeutic technique but it is utilized after crossing over to the second stage. It cant treat metastasized cancer cells in different body parts. To overcome the drawbacks of wearable devices. This can be achieved with the help of MLX90614 temperature sensors and IoT for the Cayenne cloud by Arduino. The technique we are going to use for diagnosing is signal processing. Also, this would be a non-radiation, non-invasive, comfortable, and cost-effective manner that can be conveniently used as a breast cancer analysis technique. Keywords: Breast cancer, MLX90614 temperature sensor, IoT technology, Cayenne cloud, Arduino, non-radiation, non-invasive, comfortable, cost- effective.

B. Durga Devi UG Scholar

Department of BME, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamilnadu, India

P. Jansirani UG Scholar

Department of BME, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamilnadu, India


    Breast cancer is the leading & affected cancer in the overall world especially for women. This is commonly affected by the three types of protein molecules HER2, oestrogen, & progesterone. Utmost womanhood agonizes and perishes due to this breast cancer. These patients mostly struggle with their shyness while testing, especially with a mammogram. Breast cancer detection runs down enormous time and cost. These were the problems for both doctors and patients which lead to late diagnosis of this disease. The basic step to analysis with a mammogram, but there are challenges as pain occurs while taking an image, fatty deposition, air bubbles, and indifference of benign and malignant cancer cells. To overcome these problems, defects, and uncomfortableness, we have an idea using MLX90614 temperature sensor and IoT technology. of a single one, which reduces conduction losses.


      1. Using the electrical impedance properties of the tissues, electrical impedance tomography (EIT) is a non-invasive method to scan the human breast. The primary causes of a notable rise in capacitance and conductance of malignant tumors, which leads to reduced impedance, are variations in cellular water

        content, extracellular fluids, packing density, and changes in the orientation of the cells. It is a quick, small, and affordable photography option. Since the scanning gadget doesn't release any ionizing radiation, expectant women can also use it. Large scale study will be needed.

      2. Electrical impedance tomography (EIT) and electrical impedance mapping (EIM). are the two major types of noninvasive impedance imaging techniques. EIT imaging is founded on the substantial electrical impedance variation in static and dynamic between various tissue kinds. In EIM, a steady ac voltage is typically given to the body between a big reference electrode located somewhere else on the body and an array of numerous sensing electrodes encompassing the surface of the body to be imaged. Impedance imaging is another application of magnetic induction tomography MIT utilizes the interplay of an oscillating magnetic field with conductive material as opposed to electrical interactions with the body as required by EIT or EIM.

      3. On gold screen-printed wires, two sensing devices were created. HER2 breast cancer biomarker impedimetric aptasensor for detection. Using electrochemical impedance spectroscopy, the biosensors were evaluated for their ability to identify the target protein at concentrations ranging from 1 pg/mL to 1 g/mL in phosphate buffered saline, diluted, and undiluted human blood. On gold screen- printed wires, two architectures were used to bind DNA aptamers. Ternary layers may increase the limit of detection, and electrochemical aptasensors are very hopeful for medical diagnostics.

      4. EIT (Electrical impedance Tomography) and 2D EIM (Electrical impedance Mapping) [impedance calculation and impedance mapping] Mapping Impedance: Using MATLAB's built-in picture interpolation, a flattened 2-D normalized impedance map was produced after each electrode's complex impedance was determined. After that, these images were compared to the recognized as phantom can be applied both invasive or non- invasive, and non-imaging. The separation of benign lesions and malignant tumors is difficult.

      5. Images are extracted using pre-trained CNN architectures, namely,, visual geometry, group network. System performance declines in both speed and accuracy due to the intricacy of conventional machine learning methods like pre- processing, segmentation, and feature extraction. The 2-D input data immediately updates CNN's

        design. To complete the CNN training job, a vast amount of data that is lacking in the BC area must be collected. Using TL to move the features from a sizable collection is one method to deal with this. For the intent of distinguishing both malignant and benign cells using median pooling, networks are completely connected. Both hand-crafted features along with CNN features need to be used for further improvement in classification accuracy.

      6. Five components (JUNB, YWHAB, TYROBP, NFYA, and PRDX1) selected for more study in biological samples after getting selected according to the CXCR4 related networks. A subset of 24 genes, possibly overexpressed in CTCs, was discovered by bioinformatics functional enrichment analysis. The CXCR4 chemokine receptor is important for these genes. To describe the manifestation pattern of all the molecules under examination, blood donors were employed as controls. JUNB expression was linked to a poor prognosis, enabling the creation of a novel biomarker and a possible therapeutic target for the removal of CTCs.

      7. The patients underwent ABVS, HHUS, dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) and mammography at the beginning of NAT and ABVS, HHUS and DCE. The patients underwent ABVS, HHUS, dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) and mammography at the beginning of NAT and ABVS, HHUS and DCE-MRI at the halfway point of therapy and before the surgery. ABVS have some accuracy is differ from NPV and PPV.

      8. Fluorescence microscopy finds major applications in cancer biology. The unique sample chamber, which houses the sensor technology, is made using a 3D printer. It helps to predict positive and negative values and also correlate with ABVs and MRI. Most of the parameter did not have an impact on the fluorometer output.

      9. It distinguish breast cancer with WBC (Wisconsin Breast Cancer) record. Our goal is to associate and explain that the logistic algorithm and CNN maybe used to find breast cancer with the condensed factors. Benign tumor and malignant tumors, where benign tumor is non-cancer and malignant is cancer tumor. The work hopes to assess breast cancer with 98.50% accuracy using an integrated polling ML approach.

      10. The paper is on integrate with machine learning techniques with feature selection/feature

    extraction methods and compare their performances to identify the most suitable approach. Dimensionality reduction a machine learning technique. When feature extraction is employed, data visualization and understanding are not facilitated. Further, feature extraction lessens duration of training and storage needs.


      1. Do you regularly examine your breast?

        Figure no.: 1 Examination of breast

      2. Do you feel uncomfortable to talk about with doctor/health advisor?

        Figure no.: 2 Uncomfortable

      3. Do you feel lumps/ cyst in your breast area?

        Figure no.: 3 Abnormalities

      4. Do you follow regular exercise?

        Figure no.: 4 Exercises

      5. Do you have any doubt regarding breast cancer/ breast examination?

        Figure no.: 5 Doubts regarding breast cancer

      6. Does your living area have proper awareness about breast cancer?

        Figure no.: 6 Awareness

      7. Will you check regularly if examination of breast cancer becomes household device?

        Figure no.: 7 Household devices

      8. Will you screen your breast regularly?

        Figure no.: 8 Screened their breast

      9. Difficulties faced:

        Figure no.: 9 Difficulties faced

      10. Complete Analysis of Field Survey

        Figure no.: 10 Complete analysis of field survey

      11. Temperature data:

        Figure no.: 11 Average Breast temperature

        Figure no.: 12 Average Breast temperature on meniscus days

        Figure no.: 13 Average Breast cancer temperature

        • The average breast temperature 32.7 c

          36.4. C

        • The average temperature on periods: 35.2


          Temperature ranges:

        • Benign cancer=38-39.87.C

        • Malignant cancer=40-43.6.C

    *Data taken from Mohans mendicity, Madurai from the base paper.


    Mammogram and Proton therapy are used majorly for screening and therapeutic technologies, currently which are popular technologies. The existing systems are using X-rays are passed to specify the part for viewing the cancer by mammogram. Biopsy test is used, in case of failure of mammogram. BMRI (Breast Magnetic Resonance Imaging) used for clear identification of breast cancer. The temperature variation occurred in their breast one who suffered from cancer. Hence, the IR (infrared) technologies like imaging technique, tomography, and sensor are performed. The temperature measuring system are also used in it, like thermocouple, temperature sensor, thermistor and IR thermal sensor are also performed. Even though these much techniques are available in it, the patient cannot diagnose and treated hundred percentage.


    To design a circuit for analyzation of breast cancer in advance identification. The circuit must be in simple nature and a lifelong usable one. The patient must feel free and comfortable while using it. Hence, it must be a non-contact of a breast. It should be non- invasive, non-radiation, cost-effective, and more accurate. It is used as a confirmatory test for advanced breast cancer levels.

      1. Aim

        Our aim is to find out the breast cancer identification in advance.

      2. Motivation

        • Breast cancer patient must analyse their breast in a cost-effective manner, with non-radiation, comfortably and to avoid post-analysis in their sessions.

        • Avoid post-analysis of breast cancer and Devoid rate of death of breast cancer.

        • In 2023, the WHO (World Health Organization) theme is to Close the care gap for cancer patients.

        • Our project works on breast cancer and promotes the quote of the world health organization.

      3. Objective

        • To detect breast cancer early, improving survival and lowering morbidity.

        • To find an easy analysis method that helps to enhance the comfortability of patients and a better chance for survival.

        • To reduce the cost of care.


    Figure no.: 14 Block diagram

    In this project, we use power supply as alternating current AC as 220V. This cant adopt by utilized requirements. Hence, we choose step down transformer to lower the power input source as 12V. But the sources are used to accept only direct current DC. The DC power source is done with the help of four diodes which performs forward and backward

    bias and acts as a bridge rectifier. This produce 9V DC power supply. Due to multiple conversion of power process, there is possible to occur spice errors as voltage fluctuation. On rectifying this, the capacitor is used as a filter. On holding of requirement power supply of 5V we involve regulator 7805 as producing of 5V. This all conclude as DC power supply unit with LED for identification of power utility. The Arduino UNO boards run with 5V power and temperature sensor connected with it as per I2C protocol. Arduino UNO board used because all in one as oscillator, integrated chip, microcontroller, etc. Node MCU acts as IoT module which has the ability to connect with Wi-Fi ability objects. It runs with 3.3V power inbuilt of 1.8V ohmic resistor response. From the MLX90614 temperature sensor collects the temperature data from the environment and breast into two as ambient and object temperature. The LCD unit is attached with main Arduino UNO board for notifying its output and entire error identification. Through Wi- Fi ability we can connect with mobile and laptop for receiving both sensors value with the cloud cayenne app which is the source for storing purpose up-to one year by denoting as ranges and wave form.

    1. Flow process

      Figure no.: 15 Flow chart

    2. Coding


      #include <Adafruit_MLX90614.h>

      Adafruit_MLX90614 mlx = Adafruit_MLX90614();

      int new_temp=0, new_obj=0, old_temp=0, old_obj=0;

      void setup() { Serial.begin(9600);

      while (!Serial);

      if (!mlx.begin()) {

      // Serial.println("Error connecting to MLX sensor. Check wiring.");

      while (1);



      void loop()


      new_temp=mlx.readAmbientTempC(); new_obj=mlx.readObjectTempC();

      /*if (new_temp != old_temp)


      old_temp = new_temp; Serial.print("a"); Serial.print(new_temp);


      if (new_obj != old_obj)


      old_obj = new_obj; Serial.print("o"); Serial.print(new_obj);


      /*Serial.print("m"); Serial.print(mlx.readAmbientTempF()); delay(1000);

      Serial.print("b"); Serial.print(mlx.readObjectTempF()); delay(1000);*/

      Serial.print("a"); Serial.print(new_temp); delay(1000); Serial.print("o"); Serial.print(new_obj); delay(1000); Serial.println(); delay(1000);


      delay(1000); lcd.setCursor(0, 1);

      lcd.print("BLADDER ABNORMAL");

      delay(1000); lcd.clear();




      #include <Adafruit_MLX90614.h>

      #include <LiquidCrystal.h> LiquidCrystal lcd(8, 9, 10, 11, 12, 13); Adafruit_MLX90614 mlx = Adafruit_MLX90614();

      int New_Temp=0, New_Obj=0, Old_Temp=0, Old_Obj=0;

      void setup()


      lcd.begin(16, 2); lcd.print("welcome"); delay(1000); Serial.begin(9600); while (!Serial);

      if (!mlx.begin()) {

      //Serial.println("Error connecting to MLX sensor. Check wiring.");

      while (1);



      void loop()


      lcd.clear(); lcd.setCursor(0,0); lcd.print("A:");

      lcd.pint(mlx.readAmbientTempC()); lcd.print("C");

      lcd.setCursor(9,0); lcd.print("O:");

      lcd.print(mlx.readObjectTempC()); lcd.print("C");

      /*lcd.setCursor(0,1); lcd.print("A:");

      lcd.print(mlx.readAmbientTempF()); lcd.print("F");

      lcd.setCursor(9,1); lcd.print("O:");

      lcd.print(mlx.readObjectTempF()); lcd.print("F");


      New_Temp=mlx.readAmbientTempC(); New_Obj=mlx.readObjectTempC();

      if (New_Temp != Old_Temp)


      Old_Temp = New_Temp;

      Serial.print("A"); Serial.print(New_Temp);


      if (New_Obj != Old_Obj)


      Old_Obj = New_Obj; Serial.print("O"); Serial.print(New_Obj);


      //p.W: 123456789

      int a=0,b=0,c=0,d=0,e=0,A=0,B=0,C=0,D=0, E=0,F=0;

      void setup()


      Cayenne.begin(username, password, clientID, ssid, wifiPassword); Serial.begin(9600);


      /*Serial.print("M"); Serial.print(mlx.readAmbientTempF()); delay(500);

      Serial.print("B"); Serial.print(mlx.readObjectTempF()); delay(500);*/

      Serial.print("A"); Serial.print(New_Temp); delay(200); Serial.print("O"); Serial.print(New_Obj); delay(200); Serial.println(); delay(200);


      void serialEvent()


      while (Serial.available()>0) Serial.println(Serial.readString());


      IoT CODING:

      #define CAYENNE_PRINT Serial

      #include <CayenneMQTTESP8266.h>

      // WiFi network info.

      char ssid[] ="cancerproject";

      char wifiPassword[] ="123456789"; char red1;

      // Cayenne authentication info. This should be obtained from the Cayenne Dashboard.

      char username[] = "254bebd0-d95a-11ed- 9ab8-d511caccfe8c";

      char password[] = "b38a5ae49cd57070ee97ce178e4427e6c7 7d45fc";

      char clientID[] = "34ade8d0-d95a-11ed- 8485-5b7d3ef089d0";

      // email


      void loop()








      float sen1=Serial.parseFloat();





      float sen2=Serial.parseFloat();





      float sen3=Serial.parseFloat();





      float sen4=Serial.parseFloat();





      because it is non-radiation, non-invasive and non contact of breast.


      As the size and weight of this device is less while compared with other diagnosing device, it have better sustainability.


      This prototype is made by sensors and controllers so it will be easy to manufacture. It will be of great use to the patients with its simplest design structure.


      No ethical standards or formal process in relation to it have been violated during the design of the prototype.

      Health and Safety

      The prototype of breast cancer screening is a device which is used for periodical check-up. The major aim of this device is to avoid radiations. It makes patients to detect the cancer at early stage.


      This self-monitoring device creates awareness among women for regular monitoring. The future improvement in the design of prototype will be of great use to every woman.


      No import of materials is required for this prototype.




      For self-

      diagnosing device

      IEC 60601-2-45 Medical electrical equipment – Part 2- 45: Particular requirements for basic safety and essential performance of mammographic X-ray

      equipment and

      mammographic stereotactic devices

    3. Result

      Figure no.: 15 Project Kit


    We have proposed an advanced identification of breast cancer detection as non-contact, non-invasive, non-radiation, comfortable, and cost-effective. The patient must feel free and they can use it in their home as a household device. It acts as an assenting test. The temperature sensor which we used here consists of a lens to sense the temperature. Near to the breast, we kept our senses and measures them even though we wore clothes.


    Breast divides into four lobes hence temperature sensors need to add two more for time saving. The additionality supports the testers comfort zone. Ongoing study about the breast cancer stages identify.



    Project Title

    Identification In Advance Breast Cancer Detection Using The Embedded System With IoT Technology And MLX90614 Temperature Sensor.

    Program Concentration Area

    Biomedical Instrumentation



    It is an inexpensive prototype which supports common women for regular breast screening.


    The materials used in this breast screening device dont create any negative impact upon the environment

    Table no.:1


[1] The accuracy of electrical impedance tomography for breast cancer detectionZahra, Razanejad, Gatabi. Published: A Systematic Review and Meta-Analysis. Breast J. 2022 May 26;2022:8565490. doi: 10.1155/2022/8565490. PMID:35711881; PMCID: PMC9186524.

[2] "A review of electrical impedance techniques for breast cancer detection"Y Zou et al. Med Eng Phys. Published: Breast Cancer Review in 2003 March; 25(2):79-90. DOI:10.1016/s1350-4533(02)001947.

PMID: 12538062.

[3] "Aptasensor based on screen printed electrode for breast cancer detection in undiluted human serum"Daisy C. Ferreira, Marina R. Batistuti, Bassam Bachour Junior, Marcelo Mulato. Received 18 March 2020, Revised 17 June 2020, Accepted 17

June 2020, Available online 20 June 2020, Version

of record 20 September 2020

[4] "Breast cancer detection using high density flexible electrode arrays and electrical tomography"Mathew S campisi, Cutis Barbre, Aditya Chola, Gisselle Cunningham. Conference in August 2014, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 2014:1131-4 DOI: 10.1109/EMBC.2014.6943794Source:PubMed

[5] "A novel deep learning based framework for the detection and classification of breast cancer using transfer learning"Sana ullah khan, Naveen islam, Zahoor jan, Ikram Ud Din, Joel J.P.C Rodrigues.Pattern recognition letters 125,1-6, 2019 ELSEVIER[

.22] [6] "The prognostic value of JUNB-positive CTCs in metastatic breast cancer: from bioinformatics to phenotypic characterization"Galatea Kallergi, Vasileia Tsintari, Stelios Sfakianakis, Ekaterini Bei and et al.Breast Cancer Research 21, article number:86 (2019)

[7] "Usefulness of automated breast volume scanner (ABVS) for monitoring tumor response to neo adjuvant treatment in breast cancer patients: preliminary results"A.DAngelo, P. Rinaldi, P. Belli,

R. DAmico, G. Carlino et al.European Review for Medical and Pharmacological Sciences 2019;23:225-231

[8] "Development of a lowcost and portable smart fluorometer for detecting breast cancer cells"Mohammad wajih alam, Khan A. wahid, Raghuveera kumar goel, & Kiven erique lukong.BIOMEDICAL OPTICS EXPRESS 10(2),399-410,2019

[9] "Breast Cancer Detection using Machine Learning Way"SriHari Nallamala, Pragnyaban Mishra, Suvarna Vani Koneru.International Journal of RecentTechnology and Engineering (IJRTE) – July 2019

[10] "Machine Learning Classification Techniques for Breast Cancer Diagnosis"David A. Omondiagbe, Shanmugam Veeramani, Amandeep S. Sidhu.IOP Conference Series: Materials Science and Engineering- 2019. [11] whatisbreastcancer.htm#:~:text=Breast%20cancer

%20is%20a%20disease,different%20parts%20of% 20the%20breast. [12] 986-breast-cancer

[13] sheets/detail/breast-cancer

[14] Breast [15] C8966510/

[16] 000/Cancer_incidence_estimates_for

_2022 projection.6.aspx [17] essandprevention/anatomyofthebreasts#:~:text=The

%20lobes%2C%20lobules%2C%20and%20bulbs,b reast%20and%20cover%20the%20ribs. [18] omens-health/multimedia/breast-cancer-early- stage/sls-20076628 [19] C8966510/

[20] 000/Cancer_incidence_estimates_for_2022 proj ection.6.aspx

[21] Pre-Analysis Of Breast Cancer Using Wearable Device.SakthivelSankaran,PallikondaRajasekaran Murugan,YogapriyaSridharan,BhavadharaniVeerak rishnan,VishnuvardhananGovindaraj,ArunprasathT hiyagarajanand,Preethika Immaculate Britto.Journal of Advanced Research in Dynamical and Control Systems · May 2020

DOI: 10.5373/JARDCS/V12SP2/SP20201158.