1595720640
Chest X-Ray Abnormality Classification Using Monk AI
Key Tasks
In this blog post, we will be performing three main tasks:-
- To create a binary classifier to classify the chest x-ray images as normal/abnormal.To compare three deep neural network architectures.To create a multi-label classifier to generate 14 disease labels and the respective confidence scores.
Three deep neural network architectures used by me are Vgg16, Resnet50, and MobileNet.
Table of Contents
1. Installing Monk
2. Downloading dataset
3. Importing Framework and libraries
4. Visualizing and Exploring the Samples Provided from Dataset
5. Visualizing and Exploring the Samples Provided from Dataset
6. Comparing
7. Infer
Installing Monk
We will start by setting up the Monk AI toolkit and its dependencies on the platform you are working with and I am using Google Colab as my environment.
!git clone https://github.com/Tessellate-Imaging/monk_v1.git
!cd monk_v1/installation/Misc && pip install -r requirements_colab.txt
Downloading the Dataset
After setting up the Monk toolkit the next step is to install Kaggle and download the NIH Chest X-Ray Dataset from Kaggle on our Colab notebook.
! pip install -q kaggle
To download any dataset from Kaggle we need to first download the kaggle.json file by going to MyAccount on Kaggle and download a new API. Then we will upload the JSON file on our Colab notebook.
from google.colab import files
files.upload()
Now we can download the zip file of the dataset from Kaggle and unzip it.
! mkdir ~/.kaggle
! cp kaggle.json ~/.kaggle/
! chmod 600 ~/.kaggle/kaggle.json
! kaggle datasets download -d 'nih-chest-xrays/sample'
! unzip -qq sample.zip
The dataset has a total of 15 classes (14 disease classes and 1 “no findings” class).
A Chest X-Ray Image from NIH Chest X-Ray Dataset in Kaggle
Importing Frameworks and libraries
Monk provides us three major frameworks to work with i.e., Keras, Pytorch, and Mxnet. We are using Keras framework for this project and the Pandas library is used for visualizing and exploring the dataset. To set up a working directory of a project we initialize a prototype for the framework being used.
from keras_prototype import prototype
import pandas as pd
Visualizing and Exploring the Samples provided from Dataset
Two DataFrames were made, one had multi-labeled target values which comprised of 14 disease classes and 1 “no finding” class, for binary classification of images another DataFrame was formed by replacing the disease classes and the “no finding” class by **abnormal **and **normal **respectively
$ df=pd.read_csv('sample/sample_labels.csv')
$ for i in range(len(df)):
df["Finding Labels"][i] = df["Finding Labels"][i].replace("|", ",");
$ df.to_csv("sample/kush1.csv", index=False)
$ for i in range(len(df)):
if df["Finding Labels"][i] == "No Finding":
df["Finding Labels"][i] = "Normal";
else:
df["Finding Labels"][i] = "Abnormal";
$ df.to_csv("sample/kush2.csv",index=False)
#image-classification #monk #chest-x-ray #deep-learning #computer-vision #deep learning
What is GEEK
Buddha Community
1667425440
Pdf2gerb: Perl Script Converts PDF Files to Gerber format
pdf2gerb
Perl script converts PDF files to Gerber format
Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.
The general workflow is as follows:
- Design the PCB using your favorite CAD or drawing software.
- Print the top and bottom copper and top silk screen layers to a PDF file.
- Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
- Use a Gerber viewer to double-check the output against the original PCB design.
- Make adjustments as needed.
- Submit the files to a PCB manufacturer.
Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).
See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.
pdf2gerb_cfg.pm
#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;
use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)
##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file
use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call
#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software. \nGerber files MAY CONTAIN ERRORS. Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG
use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC
use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)
#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1);
#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
.010, -.001, #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
.031, -.014, #used for vias
.041, -.020, #smallest non-filled plated hole
.051, -.025,
.056, -.029, #useful for IC pins
.070, -.033,
.075, -.040, #heavier leads
# .090, -.043, #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
.100, -.046,
.115, -.052,
.130, -.061,
.140, -.067,
.150, -.079,
.175, -.088,
.190, -.093,
.200, -.100,
.220, -.110,
.160, -.125, #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
.090, -.040, #want a .090 pad option, but use dummy hole size
.065, -.040, #.065 x .065 rect pad
.035, -.040, #.035 x .065 rect pad
#traces:
.001, #too thin for real traces; use only for board outlines
.006, #minimum real trace width; mainly used for text
.008, #mainly used for mid-sized text, not traces
.010, #minimum recommended trace width for low-current signals
.012,
.015, #moderate low-voltage current
.020, #heavier trace for power, ground (even if a lighter one is adequate)
.025,
.030, #heavy-current traces; be careful with these ones!
.040,
.050,
.060,
.080,
.100,
.120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);
#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size: parsed PDF diameter: error:
# .014 .016 +.002
# .020 .02267 +.00267
# .025 .026 +.001
# .029 .03167 +.00267
# .033 .036 +.003
# .040 .04267 +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};
#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
CIRCLE_ADJUST_MINX => 0,
CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
CIRCLE_ADJUST_MAXY => 0,
SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};
#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches
#line join/cap styles:
use constant
{
CAP_NONE => 0, #butt (none); line is exact length
CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
#number of elements in each shape type:
use constant
{
RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
rect => RECT_SHAPELEN,
line => LINE_SHAPELEN,
curve => CURVE_SHAPELEN,
circle => CIRCLE_SHAPELEN,
);
#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions
# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?
#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes.
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes
#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches
# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)
# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time
# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const
use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool
my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time
print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load
#############################################################################################
#junk/experiment:
#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html
#my $caller = "pdf2gerb::";
#sub cfg
#{
# my $proto = shift;
# my $class = ref($proto) || $proto;
# my $settings =
# {
# $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
# };
# bless($settings, $class);
# return $settings;
#}
#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;
#print STDERR "read cfg file\n";
#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names
#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }Download Details:
Author: swannman
Source Code: https://github.com/swannman/pdf2gerb
License: GPL-3.0 license
1665369120
CellularAutomata.jl: Cellular Automata Simulation toolkit for Julia
Cellular Automata
A cellular automaton is a collection of "colored" cells on a grid of specified shape that evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells. The rules are then applied iteratively for as many time steps as desired.
mathworld.wolfram.com/CellularAutomaton
Elementary CA
To generate an elementary cellular automaton, use
ca = CellularAutomaton(rule, init, gen)
where rule is the Wolfram code (integer), init is a vector containing the initial starting condition and gen is the number of generations to be computed. For a single starting cell in the middle just omit the init vector.
To generate 15 generations of elementary cellular automaton of rule 90 use
using CellularAutomata
ca90 = CellularAutomaton(90, 16)
#
# #
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Totalistic CA
For a more complex cellular automaton you can change the number of states k the cell can be and the radius r of neighbors that can influence the states. If k is changed to be larger than 2, a totalistic CA is computed where only the average value of all neighbors count. This can be done like this
ca = CellularAutomaton(993, 15, k=3)
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2 dimensional CAs
Two dimensional cellular automaton (like Conway's Game of Life) can be created by
ca = CA2d(B, S, init, gen)
where B and S are vectors that have the numbers of neighboring cells that define when cell is born or survives, init (matrix) is the initial starting condition and gen is the number of generations the CA is to be computed.
Game of life is then run for 9 generations for e.g. a turbine pattern by typing
ca = CA2d([3], [2, 3], init, 9)
1st step
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Running Tests
To run tests, execute the following command from the root folder of the repository:
julia tests/run_tests.jlDownload Details:
Author: Natj
Source Code: https://github.com/natj/CellularAutomata.jl
License: MIT license
1595720640
Chest X-Ray Abnormality Classification Using Monk AI
Key Tasks
In this blog post, we will be performing three main tasks:-
- To create a binary classifier to classify the chest x-ray images as normal/abnormal.To compare three deep neural network architectures.To create a multi-label classifier to generate 14 disease labels and the respective confidence scores.
Three deep neural network architectures used by me are Vgg16, Resnet50, and MobileNet.
Table of Contents
1. Installing Monk
2. Downloading dataset
3. Importing Framework and libraries
4. Visualizing and Exploring the Samples Provided from Dataset
5. Visualizing and Exploring the Samples Provided from Dataset
6. Comparing
7. Infer
Installing Monk
We will start by setting up the Monk AI toolkit and its dependencies on the platform you are working with and I am using Google Colab as my environment.
!git clone https://github.com/Tessellate-Imaging/monk_v1.git
!cd monk_v1/installation/Misc && pip install -r requirements_colab.txt
Downloading the Dataset
After setting up the Monk toolkit the next step is to install Kaggle and download the NIH Chest X-Ray Dataset from Kaggle on our Colab notebook.
! pip install -q kaggle
To download any dataset from Kaggle we need to first download the kaggle.json file by going to MyAccount on Kaggle and download a new API. Then we will upload the JSON file on our Colab notebook.
from google.colab import files
files.upload()
Now we can download the zip file of the dataset from Kaggle and unzip it.
! mkdir ~/.kaggle
! cp kaggle.json ~/.kaggle/
! chmod 600 ~/.kaggle/kaggle.json
! kaggle datasets download -d 'nih-chest-xrays/sample'
! unzip -qq sample.zip
The dataset has a total of 15 classes (14 disease classes and 1 “no findings” class).
A Chest X-Ray Image from NIH Chest X-Ray Dataset in Kaggle
Importing Frameworks and libraries
Monk provides us three major frameworks to work with i.e., Keras, Pytorch, and Mxnet. We are using Keras framework for this project and the Pandas library is used for visualizing and exploring the dataset. To set up a working directory of a project we initialize a prototype for the framework being used.
from keras_prototype import prototype
import pandas as pd
Visualizing and Exploring the Samples provided from Dataset
Two DataFrames were made, one had multi-labeled target values which comprised of 14 disease classes and 1 “no finding” class, for binary classification of images another DataFrame was formed by replacing the disease classes and the “no finding” class by **abnormal **and **normal **respectively
$ df=pd.read_csv('sample/sample_labels.csv')
$ for i in range(len(df)):
df["Finding Labels"][i] = df["Finding Labels"][i].replace("|", ",");
$ df.to_csv("sample/kush1.csv", index=False)
$ for i in range(len(df)):
if df["Finding Labels"][i] == "No Finding":
df["Finding Labels"][i] = "Normal";
else:
df["Finding Labels"][i] = "Abnormal";
$ df.to_csv("sample/kush2.csv",index=False)
#image-classification #monk #chest-x-ray #deep-learning #computer-vision #deep learning
1619511840
Making Sales More Efficient: Lead Qualification Using AI
If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.
AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.
#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution
1624435853
X-ray Image Classification and Model Evaluation
Pneumonia detection from chest X-ray images using Tensorflow
Kaggle has a wonderful source of chest X-ray image datasets for pneumonia and normal cases. There are significant differences between the image of a normal X-ray and an affected X-ray. Machine learning can play a pivotal role in determining the disease and significantly boost the diagnosis time as well as reduce human effort. In this article, I will walk through this dataset and classify the images with an evaluation accuracy of 90%
Image by Author
I have been motivated by the work done here on the datasets between cats and dogs and reused the code block for dataset pipeline. First we need to import the necessary packages.
#image-classification #x-rays #tensorflow #dnn #python #x-ray image classification and model evaluation