DevHeads IoT Integration Server•6d ago

How to Ensure Reliable Data and Predictions for Skin Cancer Detection Using ESP32 and TCS3200?

Am working on an AI-powered health monitoring system using an ESP32 and a TCS3200 Color Sensor to analyze skin pigmentation and detect potential cancerous cells by identifying irregularities in skin color. The sensor initializes correctly, but I am encountering inconsistent data, leading to incorrect predictions about potential skin cancer. my aim is to: - Use machine learning to analyze color data and predict the presence of cancerous cells based on irregular pigmentation. - Ensure reliable predictions with consistent data from the sensor. this is my code

4 Replies

Renuel Roberts•6d ago

import machine
import time
from machine import Pin
import random  # Simulating the machine learning prediction

# Setup for TCS3200 Color Sensor
S2 = Pin(15, Pin.OUT)
S3 = Pin(2, Pin.OUT)
out = Pin(4, Pin.IN)  # Output from sensor
S2.off()
S3.off()

# Initialize variables
color_data = {'red': 0, 'green': 0, 'blue': 0}
threshold = 50  # Example threshold for detecting cancerous cells
sensor_error = False

# Simulated AI-based cancer detection (replace with an actual ML model later)
def detect_cancerous_cells(color_data):
    # Simulating a machine learning-based prediction for cancer detection
    avg_color = (color_data['red'] + color_data['green'] + color_data['blue']) / 3
    return random.choice([True, False]) if avg_color < threshold else False

def read_color():
    # Dummy function to simulate reading from the TCS3200 sensor
    # Replace with actual sensor reading logic
    return {'red': random.randint(20, 80), 'green': random.randint(20, 80), 'blue': random.randint(20, 80)}

def calculate_average_color(color_data):
    # Calculate the average color value from red, green, and blue components
    return (color_data['red'] + color_data['green'] + color_data['blue']) / 3

# Main loop
while True:
    try:
        color_data = read_color()
        print("Color Data:", color_data)

        avg_color = calculate_average_color(color_data)
        print("Average Color Value:", avg_color)
        
        # Perform AI-based cancerous cell detection
        cancer_detected = detect_cancerous_cells(color_data)
        if cancer_detected:
            print("Warning: Potential cancerous cells detected!")
        else:
            print("Skin pigmentation is within normal range.")

    except OSError as e:
        print("Error reading color sensor data. Check wiring or sensor condition.")
        sensor_error = True
        break
    
    time.sleep(1)

import machine
import time
from machine import Pin
import random  # Simulating the machine learning prediction

# Setup for TCS3200 Color Sensor
S2 = Pin(15, Pin.OUT)
S3 = Pin(2, Pin.OUT)
out = Pin(4, Pin.IN)  # Output from sensor
S2.off()
S3.off()

# Initialize variables
color_data = {'red': 0, 'green': 0, 'blue': 0}
threshold = 50  # Example threshold for detecting cancerous cells
sensor_error = False

# Simulated AI-based cancer detection (replace with an actual ML model later)
def detect_cancerous_cells(color_data):
    # Simulating a machine learning-based prediction for cancer detection
    avg_color = (color_data['red'] + color_data['green'] + color_data['blue']) / 3
    return random.choice([True, False]) if avg_color < threshold else False

def read_color():
    # Dummy function to simulate reading from the TCS3200 sensor
    # Replace with actual sensor reading logic
    return {'red': random.randint(20, 80), 'green': random.randint(20, 80), 'blue': random.randint(20, 80)}

def calculate_average_color(color_data):
    # Calculate the average color value from red, green, and blue components
    return (color_data['red'] + color_data['green'] + color_data['blue']) / 3

# Main loop
while True:
    try:
        color_data = read_color()
        print("Color Data:", color_data)

        avg_color = calculate_average_color(color_data)
        print("Average Color Value:", avg_color)
        
        # Perform AI-based cancerous cell detection
        cancer_detected = detect_cancerous_cells(color_data)
        if cancer_detected:
            print("Warning: Potential cancerous cells detected!")
        else:
            print("Skin pigmentation is within normal range.")

    except OSError as e:
        print("Error reading color sensor data. Check wiring or sensor condition.")
        sensor_error = True
        break
    
    time.sleep(1)

Renuel Roberts•6d ago

How do I resolve these inconsistent data that sabotages the prediction of my code across multiple streams of data I have acquired for this project?

wafa_ath•6d ago

try to implement a moving average filter for sensor readings. This will smooth out fluctuations and reduce noise , check this out https://www.youtube.com/watch?v=QHWaJMncC9Q&ab_channel=VLSITechwithAnoushka

VLSI Tech with Anoushka

YouTube

Smooth Out Noisy Sensor Data with a Moving Average Filter

In this comprehensive video, we dive deep into the moving average filter, exploring its mathematics, coding implementation, and practical application with a current sensor. You'll learn how to use this powerful technique to smooth out noisy data, making it easier to work with and more accurate. Link to Github repo : https://github.com/AnoushkaTr...

Renuel Roberts•6d ago

Hello @wafa_ath thanks for the video link I went through it and followed the process it worked out well, just that now some of my image data now comes out invalid

Gaming

Programming

How to Ensure Reliable Data and Predictions for Skin Cancer Detection Using ESP32 and TCS3200?